GPS Positioning has numerous applications in the field of navigation and Geodesy.GPS positioning is mainly based on the different Geodetic Datum. This paper mainly discusses the improved datum conversion equations for the conversion of World Geodetic System (WGS-84) to Universal Transverse Mercator (UTM), vice versa and the reduction of errors introduced while datum conversion. By applying the different filters like Least Squares Algorithm (LSA), Kalman Filter (KF) and Modified Kalman Filter (MKF) a considerable improvement in consistency has been observed. Comparatively Modified Kalman Filter gives better accuracy in positioning.GPS coordinates data samples are collected in different environments like heavy traffic area, tall buildings area are taken to validate the results.
ANALYSIS OF GPS ERRORS DURING DIFFERENT TIMES IN A DAY IJORCS
This paper focuses on modeling the errors which normally degrade the accuracy of Global Poisoning System (GPS). The performance of the GPS is mainly affected by ionospheric errors. SiRF Star III single frequency receiver is used for collecting and projecting datum in World Geodetic System 1984 (WGS-84) co-ordinate form. To project the ellipsoidal model onto a map model, datum conversion from WGS-84 to Universal Transverse Mercator (UTM) form is needed. The conversion introduces errors in the datum. The variation in the errors can be observed from day to night, area to area and also due to the seasonal changes. The datum is collected from Ameerpet region of Hyderabad, which is a heavily populated area with heavy traffic and tall buildings. The variation in the datum has been observed from afternoon to evening.
ANALYSIS OF GPS ERRORS DURING DIFFERENT TIMES IN A DAY IJORCS
This paper focuses on modeling the errors which normally degrade the accuracy of Global Poisoning System (GPS). The performance of the GPS is mainly affected by ionospheric errors. SiRF Star III single frequency receiver is used for collecting and projecting datum in World Geodetic System 1984 (WGS-84) co-ordinate form. To project the ellipsoidal model onto a map model, datum conversion from WGS-84 to Universal Transverse Mercator (UTM) form is needed. The conversion introduces errors in the datum. The variation in the errors can be observed from day to night, area to area and also due to the seasonal changes. The datum is collected from Ameerpet region of Hyderabad, which is a heavily populated area with heavy traffic and tall buildings. The variation in the datum has been observed from afternoon to evening.
GPS helps us identify exact location of a place/feature in the globe. Now-a-days we can carry out survey, enter data and process data. GPS is very helpful in soil survey
Global positioning system_Surveying, Civil EngineeringA Makwana
(GPS) technology is a great boon to anyone who has the need to navigate either great or small distances.
The Global Positioning System (GPS) is a burgeoning technology, which provides unequalled accuracy and flexibility of positioning for navigation, surveying and GIS data capture.
Gps and its use in vehicle movement study in earthquake disaster management r...Mayur Rahangdale
What is GPS?
GPS Segments
Pseudo – Random Numbers (PRN)
Coarse acquisition (C/A) code
P code (Precision or Protected code)
P code (Precision or Protected code)
GPS Trilateration
EARTHQUAKE DISASTER MANAGEMENT
Disaster Management Cycle
ADVANTAGE OF GPS IN DISASTER MANAGEMENT
GPS LIMITATION IN DISASTER MANAGEMENT
HOW DOES GPS PLAY A ROLE IN EARTHQUAKE RESCUE?
Case Study - Great East Japan Earthquake in Ishinomaki City, Japan -11 March 2011.
GPS helps us identify exact location of a place/feature in the globe. Now-a-days we can carry out survey, enter data and process data. GPS is very helpful in soil survey
Global positioning system_Surveying, Civil EngineeringA Makwana
(GPS) technology is a great boon to anyone who has the need to navigate either great or small distances.
The Global Positioning System (GPS) is a burgeoning technology, which provides unequalled accuracy and flexibility of positioning for navigation, surveying and GIS data capture.
Gps and its use in vehicle movement study in earthquake disaster management r...Mayur Rahangdale
What is GPS?
GPS Segments
Pseudo – Random Numbers (PRN)
Coarse acquisition (C/A) code
P code (Precision or Protected code)
P code (Precision or Protected code)
GPS Trilateration
EARTHQUAKE DISASTER MANAGEMENT
Disaster Management Cycle
ADVANTAGE OF GPS IN DISASTER MANAGEMENT
GPS LIMITATION IN DISASTER MANAGEMENT
HOW DOES GPS PLAY A ROLE IN EARTHQUAKE RESCUE?
Case Study - Great East Japan Earthquake in Ishinomaki City, Japan -11 March 2011.
Global Positioning System (GPS) is a satellite based navigation system that can provide people who use it with their exact position on Earth, tell them how to get to another location, how fast they are moving, where they have been, how far they have gone, what time it is. GPS was originally designed to help the U.S. military with finding the accurate location of their soldiers, vehicles, planes and ships around the world. Now, GPS is used in cellular phones, navigation and map making.
Space segmentsGPS satellites fly in medium Earth orbit (MEO) at an altitude of approximately 20,200 km (12,550 miles). Each satellite circles the Earth twice a day.The satellites in the GPS constellation are arranged into six equally-spaced orbital planes surrounding the Earth. Each plane contains four "slots" occupied by baseline satellites. This 24-slot arrangement ensures users can view at least four satellites from virtually any point on the planet.
The control segment
The control segment of the GPS system consists of a worldwide network of tracking stations.
The master control station (MCS) located in the United States at Colorado Springs, Colorado.
The primary task of the operational control segment is tracking the GPS satellites in order to determine and predict satellite locations, system integrity, behavior of the satellite atomic clocks, atmospheric data, the satellite almanac, and other considerations.
The User segment
The user segment includes all military and civilian users. With a GPS receiver connected to a GPS antenna, a user can receive the GPS signals, which can be used to determine his or her position anywhere in the world. GPS is currently available to all users worldwide at no direct charge.
How it work?When a GPS receiver is first turned on, it downloads orbit information from all the satellites called an almanac.Once this information is downloaded, it is stored in the receiver’s memory for future use. The GPS receiver calculates the distance from each satellite to the receiver by using the distance formula: distance = velocity x time.The receiver determines position by using triangulation. When it receives signals from at least three satellites the receiver should be able to calculate its approximate position (a 2D position). The receiver needs at least four or more satellites to calculate a more accurate 3D position. The position can be reported in latitude/longitude.
The two GPS codes are;-
Coarse acquisition (or C/A-code)
Precision (or P-code).
The C/A-code is modulated onto the L1 carrier only, while the P-code is modulated onto both the L1 and the L2 carriers. This modulation is called biphase modulation, because the carrier phase is shifted by 180° when the code value changes from zero to one or from one to zero.
Source of GPS error
Satellite clock errors: Caused by slight discrepancies in each satellite’s four atomic clocks. Errors are monitored and corrected by the Master Control Station.
Orbit errors:Satellite orbits.
Differentiation between Global and Local Datum from Different aspect Nzar Braim
Differentiation between Global and Local Datum from Different aspect
Spatial professionals are required to deal with an increasingly wide range
of positioning information obtained from various sources including
terrestrial surveying, Global Navigation Satellite System (GNSS)
observations and online GNSS processing services. These positions refer
to a multitude of local, national, and global datums. A clear understanding
of the different coordinate reference systems and datums in use today and
the appropriate transformations between these are therefore essential to
ensure rigorous consideration of reference frame variations to
produce high-quality outcomes in spatial data analysis tasks.
Global Positioning System (GPS) is the only system today able to show one’s own position on the earth any time in any weather, anywhere. This paper addresses this satellite based navigation system at length. The different segments of GPS viz. space segment, control segment, user segment are discussed. In addition, how this amazing system GPS works, is clearly described. The various errors that degrade the performance of GPS are also included. DIFFERENTIAL GPS, which is used to improve the accuracy of measurements, is also studied. The need, working and implementation of DGPS are discussed at length. Finally, the paper ends with advanced application of GPS.
Due to availability of internet and evolution of embedded devices, Internet of things can be useful to contribute in energy domain. The Internet of Things (IoT) will deliver a smarter grid to enable more information and connectivity throughout the infrastructure and to homes. Through the IoT, consumers, manufacturers and utility providers will come across new ways to manage devices and ultimately conserve resources and save money by using smart meters, home gateways, smart plugs and connected appliances. The future smart home, various devices will be able to measure and share their energy consumption, and actively participate in house-wide or building wide energy management systems. This paper discusses the different approaches being taken worldwide to connect the smart grid. Full system solutions can be developed by combining hardware and software to address some of the challenges in building a smarter and more connected smart grid.
A Survey Report on : Security & Challenges in Internet of Thingsijsrd.com
In the era of computing technology, Internet of Things (IoT) devices are now popular in each and every domains like e-governance, e-Health, e-Home, e-Commerce, and e-Trafficking etc. Iot is spreading from small to large applications in all fields like Smart Cities, Smart Grids, Smart Transportation. As on one side IoT provide facilities and services for the society. On the other hand, IoT security is also a crucial issues.IoT security is an area which totally concerned for giving security to connected devices and networks in the IoT .As, IoT is vast area with usability, performance, security, and reliability as a major challenges in it. The growth of the IoT is exponentially increases as driven by market pressures, which proportionally increases the security threats involved in IoT The relationship between the security and billions of devices connecting to the Internet cannot be described with existing mathematical methods. In this paper, we explore the opportunities possible in the IoT with security threats and challenges associated with it.
In today’s emerging world of Internet, each and every thing is supposed to be in connected mode with the help of billions of smart devices. By connecting all the devises used in our day to day life, make our life trouble less and easy. We are incorporated in a world where we are used to have smart phones, smart cars, smart gadgets, smart homes and smart cities. Different institutes and researchers are working for creating a smart world for us but real question which we need to emphasis on is how to make dumb devises talk with uncommon hardware and communication technology. For the same what kind of mechanism to use with various protocols and less human interaction. The purpose is to provide the key area for application of IoT and a platform on which various devices having different mechanism and protocols can communicate with an integrated architecture.
Study on Issues in Managing and Protecting Data of IOTijsrd.com
This paper discusses variety of issues for preserving and managing data produced by IoT. Every second large amount of data are added or updated in the IoT databases across the heterogeneous environment. While managing the data each phase of data processing for IoT data is exigent like storing data, querying, indexing, transaction management and failure handling. We also refer to the problem of data integration and protection as data requires to be fit in single layout and travel securely as they arrive in the pool from diversified sources in different structure. Finally, we confer a standardized pathway to manage and to defend data in consistent manner.
Interactive Technologies for Improving Quality of Education to Build Collabor...ijsrd.com
Today with advancement in Information Communication Technology (ICT) the way the education is being delivered is seeing a paradigm shift from boring classroom lectures to interactive applications such as 2-D and 3-D learning content, animations, live videos, response systems, interactive panels, education games, virtual laboratories and collaborative research (data gathering and analysis) etc. Engineering is emerging with more innovative solutions in the field of education and bringing out their innovative products to improve education delivery. The academic institutes which were once hesitant to use such technology are now looking forward to such innovations. They are adopting the new ways as they are realizing the vast benefits of using such methods and technology. The benefits are better comprehensibility, improved learning efficiency of students, and access to vast knowledge resources, geographical reach, quick feedback, accountability and quality research. This paper focuses on how engineering can leverage the latest technology and build a collaborative learning environment which can then be integrated with the national e-learning grid.
Internet of Things - Paradigm Shift of Future Internet Application for Specia...ijsrd.com
In the world more than 15% people are living with disability that also include children below age of 10 years. Due to lack of independent support services specially abled (handicap) people overly rely on other people for their basic needs, that excludes them from being financially and socially active. The Internet of Things (IoT) can give support system and a better quality of life as well as participation in routine and day to day life. For this purpose, the future solutions for current problems has been introduced in this paper. Daunting challenges have been considered as future research and glimpse of the IoT for specially abled person is given in the paper.
A Study of the Adverse Effects of IoT on Student's Lifeijsrd.com
Internet of things (IoT) is the most powerful invention and if used in the positive direction, internet can prove to be very productive. But, now a days, due to the social networking sites such as Face book, WhatsApp, twitter, hike etc. internet is producing adverse effects on the student life, especially those students studying at college Level. As it is rightly said, something which has some positive effects also has some of the negative effects on the other hand. In this article, we are discussing some adverse effects of IoT on student’s life.
Pedagogy for Effective use of ICT in English Language Learningijsrd.com
The use of information and communications technology (ICT) in education is a relatively new phenomenon and it has been the educational researchers' focus of attention for more than two decades. Educators and researchers examine the challenges of using ICT and think of new ways to integrate ICT into the curriculum. However, there are some barriers for the teachers that prevent them to use ICT in the classroom and develop supporting materials through ICT. The purpose of this study is to examine the high school English teachers’ perceptions of the factors discouraging teachers to use ICT in the classroom.
In recent years usage of private vehicles create urban traffic more and more crowded. As result traffic becomes one of the important problems in big cities in all over the world. Some of the traffic concerns are traffic jam and accidents which have caused a huge waste of time, more fuel consumption and more pollution. Time is very important parameter in routine life. The main problem faced by the people is real time routing. Our solution Virtual Eye will provide the current updates as in the real time scenario of the specific route. This research paper presents smart traffic navigation system, based on Internet of Things, which is featured by low cost, high compatibility, easy to upgrade, to replace traditional traffic management system and the proposed system can improve road traffic tremendously.
Ontological Model of Educational Programs in Computer Science (Bachelor and M...ijsrd.com
In this work there is illustrated an ontological model of educational programs in computer science for bachelor and master degrees in Computer science and for master educational program “Computer science as second competence†by Tempus project PROMIS.
Understanding IoT Management for Smart Refrigeratorijsrd.com
Lately the concept of Internet of Things (IoT) is being more elaborated and devices and databases are proposed thereby to meet the need of an Internet of Things scenario. IoT is being considered to be an integral part of smart house where devices will be connected to each other and also react upon certain environmental input. This will eventually include the home refrigerator, air conditioner, lights, heater and such other home appliances. Therefore, we focus our research on the database part for such an IoT’ fridge which we called as smart Fridge. We describe the potentials achievable through a database for an IoT refrigerator to manage the refrigerator food and also aid the creation of a monthly budget of the house for a family. The paper aims at the data management issue based on a proposed design for an intelligent refrigerator leveraging the sensor technology and the wireless communication technology. The refrigerator which identifies products by reading the barcodes or RFID tags is proposed to order the required products by connecting to the Internet. Thus the goal of this paper is to minimize human interaction to maintain the daily life events.
DESIGN AND ANALYSIS OF DOUBLE WISHBONE SUSPENSION SYSTEM USING FINITE ELEMENT...ijsrd.com
Double wishbone designs allow the engineer to carefully control the motion of the wheel throughout suspension travel. 3-D model of the Lower Wishbone Arm is prepared by using CAD software for modal and stress analysis. The forces and moments are used as the boundary conditions for finite element model of the wishbone arm. By using these boundary conditions static analysis is carried out. Then making the load as a function of time; quasi-static analysis of the wishbone arm is carried out. A finite element based optimization is used to optimize the design of lower wishbone arm. Topology optimization and material optimization techniques are used to optimize lower wishbone arm design.
A Review: Microwave Energy for materials processingijsrd.com
Microwave energy is a latest largest growing technique for material processing. This paper presents a review of microwave technologies used for material processing and its use for industrial applications. Advantages in using microwave energy for processing material include rapid heating, high heating efficiency, heating uniformity and clean energy. The microwave heating has various characteristics and due to which it has been become popular for heating low temperature applications to high temperature applications. In recent years this novel technique has been successfully utilized for the processing of metallic materials. Many researchers have reported microwave energy for sintering, joining and cladding of metallic materials. The aim of this paper is to show the use of microwave energy not only for non-metallic materials but also the metallic materials. The ability to process metals with microwave could assist in the manufacturing of high performance metal parts desired in many industries, for example in automotive and aeronautical industries.
Web Usage Mining: A Survey on User's Navigation Pattern from Web Logsijsrd.com
With an expontial growth of World Wide Web, there are so many information overloaded and it became hard to find out data according to need. Web usage mining is a part of web mining, which deal with automatic discovery of user navigation pattern from web log. This paper presents an overview of web mining and also provide navigation pattern from classification and clustering algorithm for web usage mining. Web usage mining contain three important task namely data preprocessing, pattern discovery and pattern analysis based on discovered pattern. And also contain the comparative study of web mining techniques.
APPLICATION OF STATCOM to IMPROVED DYNAMIC PERFORMANCE OF POWER SYSTEMijsrd.com
Application of FACTS controller called Static Synchronous Compensator STATCOM to improve the performance of power grid with Wind Farms is investigated .The essential feature of the STATCOM is that it has the ability to absorb or inject fastly the reactive power with power grid . Therefore the voltage regulation of the power grid with STATCOM FACTS device is achieved. Moreover restoring the stability of the power system having wind farm after occurring severe disturbance such as faults or wind farm mechanical power variation is obtained with STATCOM controller . The dynamic model of the power system having wind farm controlled by proposed STATCOM is developed . To validate the powerful of the STATCOM FACTS controller, the studied power system is simulated and subjected to different severe disturbances. The results prove the effectiveness of the proposed STATCOM controller in terms of fast damping the power system oscillations and restoring the power system stability.
Making model of dual axis solar tracking with Maximum Power Point Trackingijsrd.com
Now a days solar harvesting is more popular. As the popularity become higher the material quality and solar tracking methods are more improved. There are several factors affecting the solar system. Major influence on solar cell, intensity of source radiation and storage techniques The materials used in solar cell manufacturing limit the efficiency of solar cell. This makes it particularly difficult to make considerable improvements in the performance of the cell, and hence restricts the efficiency of the overall collection process. Therefore, the most attainable maximum power point tracking method of improving the performance of solar power collection is to increase the mean intensity of radiation received from the source used. The purposed of tracking system controls elevation and orientation angles of solar panels such that the panels always maintain perpendicular to the sunlight. The measured variables of our automatic system were compared with those of a fixed angle PV system. As a result of the experiment, the voltage generated by the proposed tracking system has an overall of about 28.11% more than the fixed angle PV system. There are three major approaches for maximizing power extraction in medium and large scale systems. They are sun tracking, maximum power point (MPP) tracking or both.
A REVIEW PAPER ON PERFORMANCE AND EMISSION TEST OF 4 STROKE DIESEL ENGINE USI...ijsrd.com
In day today's relevance, it is mandatory to device the usage of diesel in an economic way. In present scenario, the very low combustion efficiency of CI engine leads to poor performance of engine and produces emission due to incomplete combustion. Study of research papers is focused on the improvement in efficiency of the engine and reduction in emissions by adding ethanol in a diesel with different blends like 5%, 10%, 15%, 20%, 25% and 30% by volume. The performance and emission characteristics of the engine are tested observed using blended fuels and comparative assessment is done with the performance and emission characteristics of engine using pure diesel.
Study and Review on Various Current Comparatorsijsrd.com
This paper presents study and review on various current comparators. It also describes low voltage current comparator using flipped voltage follower (FVF) to obtain the single supply voltage. This circuit has short propagation delay and occupies a small chip area as compare to other current comparators. The results of this circuit has obtained using PSpice simulator for 0.18 μm CMOS technology and a comparison has been performed with its non FVF counterpart to contrast its effectiveness, simplicity, compactness and low power consumption.
Reducing Silicon Real Estate and Switching Activity Using Low Power Test Patt...ijsrd.com
Power dissipation is a challenging problem for today's system-on-chip design and test. This paper presents a novel architecture which generates the test patterns with reduced switching activities; it has the advantage of low test power and low hardware overhead. The proposed LP-TPG (test pattern generator) structure consists of modified low power linear feedback shift register (LP-LFSR), m-bit counter, gray counter, NOR-gate structure and XOR-array. The seed generated from LP-LFSR is EXCLUSIVE-OR ed with the data generated from gray code generator. The XOR result of the sequence is single input changing (SIC) sequence, in turn reduces the switching activity and so power dissipation will be very less. The proposed architecture is simulated using Modelsim and synthesized using Xilinx ISE9.2.The Xilinx chip scope tool will be used to test the logic running on FPGA.
Defending Reactive Jammers in WSN using a Trigger Identification Service.ijsrd.com
In the last decade, the greatest threat to the wireless sensor network has been Reactive Jamming Attack because it is difficult to be disclosed and defend as well as due to its mass destruction to legitimate sensor communications. As discussed above about the Reactive Jammers Nodes, a new scheme to deactivate them efficiently is by identifying all trigger nodes, where transmissions invoke the jammer nodes, which has been proposed and developed. Due to this identification mechanism, many existing reactive jamming defending schemes can be benefited. This Trigger Identification can also work as an application layer .In this paper, on one side we provide the several optimization problems to provide complete trigger identification service framework for unreliable wireless sensor networks and on the other side we also provide an improved algorithm with regard to two sophisticated jamming models, in order to enhance its robustness for various network scenarios.
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
GPS Datum Conversion and Improvement in GPS Accuracy
1. IJSRD - International Journal for Scientific Research & Development| Vol. 2, Issue 08, 2014 | ISSN (online): 2321-0613
All rights reserved by www.ijsrd.com 111
GPS Datum Conversion and Improvement in GPS Accuracy
Sk.Mona1
Ms. Swapna Raghunath2
1
Student 2
Associate Professor
1,2
Department of Electronics and Communication Engineering
1,2
G. Narayanamma Institute of Technology & Science, Andhra Pradesh, India
Abstract— GPS Positioning has numerous applications in
the field of navigation and Geodesy.GPS positioning is
mainly based on the different Geodetic Datum. This paper
mainly discusses the improved datum conversion equations
for the conversion of World Geodetic System (WGS-84) to
Universal Transverse Mercator (UTM), vice versa and the
reduction of errors introduced while datum conversion. By
applying the different filters like Least Squares Algorithm
(LSA), Kalman Filter (KF) and Modified Kalman Filter
(MKF) a considerable improvement in consistency has been
observed. Comparatively Modified Kalman Filter gives
better accuracy in positioning.GPS coordinates data samples
are collected in different environments like heavy traffic
area, tall buildings area are taken to validate the results.
Keywords: Global Positioning System, World Geodetic
System, Universal Transverse Mercator, Least Squares
Algorithm, Kalman Filter, Modified Kalman Filter
I. INTRODUCTION
The enormous demand for the storage, analysis and display
of complex and voluminous data has led, in recent years, to
the use of Geographic Information System (GIS) for
effective data handling, analyzing and transferring the
information around the world. The heart of GIS is the
Global Positioning System (GPS).
GPS is a burgeoning technology, which provides
unequalled accuracy and flexibility of positioning for
navigation, surveying and GIS data capture. GPS is a Global
Navigation Satellite System (GNSS) developed by the
United States Department of Defense. It uses a constellation
of between 24 and 32 Medium Earth Orbit satellites that
transmit precise microwave signals, which enable GPS
receivers to determine their current location in longitude,
latitude, and altitude, time, and velocity.
Using the Global Positioning System (GPS, a
process used to establish a position at any point on the
globe) the following two values can be determined
anywhere on Earth
(1) One’s exact location (longitude , latitude and
height co-ordinates) accurate to within a range
of 20m to approx 1mm.
(2) The precise time (Universal Time Coordinated,
UTC) accurate to within a range of 60ns to approx
5ns Speed and direction of travel (course) can be
derived from these co-ordinates as well as the time.
The coordinates and time values are determined by
28 satellites orbiting the Earth.
The NAVSTAR Global Positioning System (GPS)
is a satellite-based radio-positioning and time transfer
system designed, financed, deployed, and operated by the
U.S. Department of Defense. GPS has also demonstrated a
significant benefit to the civilian community who are
applying GPS to a rapidly expanding number of
applications. What attracts us to GPS is:
The relatively high positioning accuracies, from
tens of meters down to the millimeter level.
The capability of determining velocity and time, to
an accuracy commensurate with position.
The signals are available to users anywhere on the
globe: in the air, on the ground, or at sea.
It is a positioning system with no user charges,
which simply requires the use of relatively low cost
hardware.
It is an all-weather system, available 24 hours a
day.
The position information is in three dimensions,
that is, vertical as well as horizontal information is
provided.
GPS receivers are used for positioning, locating,
navigating, surveying and determining the time and are
employed both by private individuals (e.g.for leisure
activities, such as trekking, balloon flights and cross-country
skiing etc.) and companies (surveying, determining the time,
navigation, vehicle monitoring etc.). GPS (the full
description is: NAVigation System with Timing And
Ranging Global Positioning System, NAVSTARGPS) was
developed by the U.S. Department of Defense (DoD) and
can be used both by civilians and military personnel.
The civil signal SPS (Standard Positioning Service)
can be used freely by the general public, whilst the military
signal PPS (Precise Positioning Service) can be used by
authorized government agencies. There are currently 28
operational satellites orbiting the Earth at a height of 20,180
km on 6 different orbital planes. Their orbits are inclined at
55° to the equator, ensuring that at least 4 satellites are in
radio communication with any point on the planet. Each
satellite orbits the Earth in approximately 12 hours and has
four atomic clocks on board.
The GPS is a space-based satellite navigation
system that provides location and time information in all
weather conditions, anywhere on or near the Earth where
there is an unobstructed line of sight to four or more GPS
satellites. The system provides critical capabilities to
military, civil and commercial users around the world. It is
maintained by the United States government and is freely
accessible to anyone with a GPS receiver.
GPS is based on the World Geodetic System 1984
(WGS-84).The coordinates received from the Global
Positioning System (GPS) are prone to various errors
like ionospheric delays, satellite and receiver clock
errors, multipath errors, satellite geometry, signal
propagation errors, receiver errors, etc. The current
standard reference coordinate system used by the GPS
is the WGS84 system in which any point on the earth is
identified by its latitude and longitude.
2. GPS Datum Conversion and Improvement in GPS Accuracy
(IJSRD/Vol. 2/Issue 08/2014/027)
All rights reserved by www.ijsrd.com 112
On a conformal map, meridians and parallels intersect at
right angles, and the scale at any point on the map is the
same in any direction, although it will vary from point to
point. Conformal maps therefore allow the analysis, control
or recording of motion and angular relationships. Hence
they are essential for the generation of navigational charts,
meteorological charts and topographic maps. An example of
a conformal projection is the Transverse Mercator
projection, which is used extensively around the world as a
basis for grid coordinates and is therefore treated in more
detail here. This projection mathematically derived and
utilizes a cylinder that is tangent to a chosen meridian,
called the central meridian (CM) as shown in Figure 1.1 The
scale is therefore true (i.e. unity) along the central meridian
but increases with increasing distance from it, thereby
causing a growing distortion in scale.
Fig. 1.1: Universal Transvese Mercator (UTM) System
The Transverse Mercator projection is most
appropriate for regions exhibiting a large north-south extent
but small east-west extent. However, by splitting up the area
to be mapped into longitudinal zones of limited extent and
merging the resulting plane maps, the entire world can be
mapped with minimal distortion.
The Universal Transverse Mercator (UTM)
projection utilises a zone width of 6° and ensures that the
scale is very close to unity across the entire zone by defining
a central scale factor of 0.9996 for the CM which results in a
scale of 1.0010 at the zone boundary located 3° away from
the CM. The UTM projection divides the world into 60
zones, zone 1 having a CM at longitude 177°W, while the
latitudinal extent of each zone is 80°S to 84°N, indicated by
20 bands labeled C to X with the exclusion of I and O for
obvious reasons. All latitude bands are 8° wide, except the
most northerly (X) which is 12° wide to allow Greenland to
be mapped in its entirety.
The increasing distortion in scale evident at high
latitudes is caused by the north-south gridlines not
converging at the poles, i.e. the poles would be projected as
lines rather than points. The island of Tasmania, e.g., is
located in zone 55G. Note that while the latitude extent is
generally part of the coordinate display in most GPS
receivers, in a GIS environment it is often replaced by N or
S to indicate the hemisphere when a global UTM system is
used.
WGS84 [7] is a 3-dimensional angular
coordinate system, but when a point has to be mapped
i.e. 2-dimensional projection on a paper, then the preferred
coordinate system is the UTM. Hence, the necessity to
convert the WGS84 coordinates to their corresponding UTM
values. The coordinate conversion causes some error to be
produced in the resulting UTM values [12].
First the conversion of the coordinates from
WGS84 to UTM has been done using asset of conversion
equations proposed by Steven Dutch which showed a large
error of about 100m in Eastings and 300m in Northings
coordinates.
Having to present parts of the Earth, which is
originally an ellipsoid, on the paper, which is plane (bi-
dimensional), it is necessary to project the geographic
coordinates onto the flat surface of a map, using one of the
existing methods. The cartography uses different methods of
projecting the geographic coordinates. Even nowadays,
different countries apply different, local geodetic systems
(datum). This is due to historic reasons and the need of the
countries to maximally adopt the geodetic system to their
local circumstances. This is why a large number of geodetic
systems were developed. Then the conversion of the
coordinates from WGS84 to UTM has been done using
a set of equations proposed by Milenko.T.Ostojic [7] to
reduce the error in the Eastings to 25m and Northings to
100m which shows the large improvement in accuracy of
the position [5].
WGS84 was adopted as standard world geodetic
system, but its application will be gradual, in view of a
rather rich geodetic material which exists in the world. The
proposed technique by Milenko.T.Ostojic defined more
closely a mathematical device which provides conversion of
geodetic coordinates into coordinate system in the plane and
vice versa. The existing techniques discuss the reduction of
the above said coordinate conversion error using a Least
squares Algorithm and Kalman filter. MATLAB software
has been used for the coordinate conversion and the design
of the filter. The estimation of the error and its reduction
using a Least square Algorithm [3] and Kalman filter and
has been done in the existing method. After applying Least
squares Algorithm and Kalman filter to the UTM values,
there was a visible improvement in the consistency of the
coordinates.
The Least squares Algorithm called LOWESS
algorithm is used for smoothening the GPS coordinates for
accurate position estimation. The name "LOWESS" is
derived from the term "locally weighted scatter plot
smooth," as this method uses locally weighted linear
regression to smooth data. The smoothing process is
considered local because each smoothed value is determined
by neighbouring data points. The process is weighted
because a regression weight function is defined for the data
points. In addition to the regression weight function, the
regression model lowess uses a linear polynomial. Better
smoothening of GPS data can be done using Kalman filter
compared to LOWESS algorithm
The Kalman filter is extensively used for state
estimation for linear systems under Gaussian noise. When
non-Gaussian Levy noise is present, the conventional
Kalman filter may fail to be effective due to the fact that the
non-Gaussian Levy noise may have infinite variance. So a
Modified Kalman filter [6] for linear systems with non-
3. GPS Datum Conversion and Improvement in GPS Accuracy
(IJSRD/Vol. 2/Issue 08/2014/027)
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Gaussian Levy noise is devised. It works effectively with
reasonable computational cost.
This paper mainly discusses the Modified Kalman
Filter which can be applied to linear systems which reduces
the error in conversion from WGS84 to UTM by 1 to 3
meters in Eastings and Northings when compared to Kalman
Filter, which is about 50% reduction of the conversion error
when compared to the Steven Dutch’s WGS84 to UTM
conversion error.
Then the reverse conversion of UTM to WGS84 is
done using the conversion equations given by
Milenko.T.Ostojic. The reverse conversion coordinates is
compared with the WGS84 coordinates from the receiver to
know the accuracy of the data.
II. IMPROVED GPS DATUM CONVERSION METHODOLOGY
This section introduces the improved technique of datum
conversion i.e WGS-84 to UTM and vice versa.
A. WGS-84 to UTM Conversion
These equations define more closely a mathematical device
which provides conversion of geodetic coordinates into a
coordinate system in the plane and vice versa. In this, the
point on the ellipsoid is defined by its latitude (φ), longitude
(λ) and it is projected onto the plane (x, y).This requires
projection conformity.
In the Transverse Mercator projection, the
projection of a point from ellipsoid (φ, λ) is carried out onto
a plane (y, x), which leans on the ellipsoid. The convention
is to give y first, because of the correspondence (y, x) with
(φ, λ). Since the middle meridian is projected without
distortion, the longitude is given as value (l), which is the
longitude counted from the middle meridian. The projection
is performed according to the following formulas.
( ) ( ) ( )(
) ( )(
) ( )(
) (2.1)
( ) ( )( )
( )( )
( )( ) (2.2)
where ( ) is ellipsoid length of the meridian arc
from equator to the observed point is calculated according to
the following formula:
( )= ( ( )) ( ) ( )
( )
N is the curvature radius per vertical and equals to
√
= ( ) , is the eccentricity equal to ( )⁄
( ), auxilary value
, longitude in respect to the central meridian
=longitude of the central meridian
The coefficients are as given as follows
( )
n= (a-b) / (a+b)
Thus the WGS84 coordinates can be converted to
UTM coordinates by the equations mentioned above.
B. UTM to WGS-84 Conversion
Inverse projection of the point in the plane ( y, x) , onto the
ellipsoid (φ ,λ), is carried out as follows:
( ) (
) (
) (
)
(2.3)
( ) ( )
( )
( )
( )
( )
( )
(2.4)
Where
( ) ( ) ( )
( )
The coefficients are calculated as follows
( )
Where a and b in equation are the major and minor
axes of the reference ellipsoid given by a=6377397.155,
b=6356149.9884347 respectively.
Thus the reverse conversion that is UTM to WGS-
84 can be done using the formulae given above where x & y
represent Eastings and Northings respectively and λ & ϕ
represents Longitude and Latitude respectively.
III. ERROR REDUCTION IN GPS DATUM CONVERSION
A. Least Squares Algorithm
Least squares Algorithm is also known as Local regression
smoothing algorithm. The name "LOWESS" is derived from
the term "locally weighted scatter plot smooth," as it uses
locally weighted linear regression to smooth data. The
smoothing process is considered local because each
smoothed value is determined by neighbouring data points.
The process is weighted because a regression weight
function is defined for the data points. In addition to the
regression weight function, a robust weight function, can be
used which makes the process resistant to outliers. Finally,
4. GPS Datum Conversion and Improvement in GPS Accuracy
(IJSRD/Vol. 2/Issue 08/2014/027)
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the model used in the regression LOWESS uses a linear
polynomial.
The regression smoothing and robust smoothing
procedures are described in detail below.
B. Local Regression Smoothing Procedure
The local regression smoothing process follows these steps
for each data point:
(1) Compute the regression weights for each data point
in the span. The weights are given by the tricube
function shown below.
(3.1)
x is the predictor value associated with the response
value to be smoothed, xi are the nearest neighbours
of x as defined by the span, and d(x) is the distance
along the abscissa from x to the most distant
predictor value within the span. The weights have
these characteristics:
The data point to be smoothed has the
largest weight and the most influence on
the fit.
Data points outside the span have zero
weight and no influence on the fit.
(2) A weighted linear least squares regression is
performed. For LOWESS, the regression uses a
first degree polynomial.
(3) The smoothed value is given by the weighted
regression at the predictor value of interest.
C. Kalman Filter
Kalman filter is a linear recursive filtering technique that
estimates the user position from the initial state of the
system, statistics of system noise and sensor noise
measurements (Malleswari B.L et al, 2009). The Kalman
filter estimates a process state at some time and also
obtains noisy measurement of the state as feedback
The Kalman filter, also known as linear quadratic estimation
(LQE), is an algorithm that uses a series of measurements
observed over time, containing noise (random variations)
and other inaccuracies, and produces estimates of unknown
variables that tend to be more precise than those based on a
single measurement alone. More formally, the Kalman filter
operates recursively on streams of noisy input data to
produce a statistically optimal estimate of the underlying
system state. The filter is named for Rudolf (Rudy) E.
Kalman, one of the primary developers of its theory.
At each cycle, the state estimate is updated by
combining new measurements with the predicted state
estimate from previous measurements.
The x is defined as the a priori state
estimate at time step k when the process prior to step k is
known, and the a posteriori state estimate at step k when the
measurement is known. The priori and a posteriori estimates
errors can be defined as:
Where =priori estimate error.
=posterior error estimate error.
= present state estimate at time k.
= posteriori state estimate at step k.
= priori state estimate at step k.
The priori estimate error co-variance is,
Where E [ ] = expectation (mean)
The a posteriori estimate error covariance is,
The next step involves finding an equation that
computes an a posteriori state estimate as a linear
combination of an a priori estimate and a weighted
difference between an actual measurement and a
measurement prediction
( ) (3.2)
Where =optimal Kalman gain
H= is the observation matrix which maps the true
state space into the observed space
=the observation or measurement at time k and is
given by
(3.3)
= is the observation noise which is assumed to be
zero mean Gaussian white noise with covariance
The Kalman gain calculated from the equation:
( ) (3.4)
Where = priori estimate error covariance
=measurement error
covariance
The Kalman Filter estimates a process by using a
feedback control like form. The operation can be described
as the process is estimated by the filter at some point of time
and the feedback is obtained in the form of noisy
measurements. The Kalman filter equations can be divided
into two categories: time update equations and measurement
update equations. To obtain the a priori estimates for the
next time step the time update equations project forward (in
time) the current state and error covariance estimates. The
measurement update equations get the feedback to obtain an
improved a posteriori estimate incorporating a new
measurement into the a priori estimate.
The notation shows the estimate of X at time
n, when observations till time m are obtained.
The two variables that can represent the filter:
, the a posteriori state estimate at time k
, the a posteriori error covariance matrix (a measure of
the estimated accuracy of the state estimate).
The prediction equations of Kalman filter are as given
below:
Predicted (a priori) state is calculated as given in equation
x x u
Predicted (a priori) estimate covariance is estimated as given
in equation
- - -
T
The Updating equations of Kalman Filter are as given
below
Innovation or measurement residual which is used to
calculate Updated estimate is as given in equation
y - x -
Innovation (or residual) covariance which is used to
calculate Kalman Gain are as given as in equation
S -
T
The Optimal Kalman gain is calculated as given in equation
5. GPS Datum Conversion and Improvement in GPS Accuracy
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All rights reserved by www.ijsrd.com 115
-
T
S
-
The Updated (a posteriori) state estimate is given by
equation
x x -
y
The Updated (a posteriori) estimate covariance is given by
equation
( - ) -
(3.5)
Where = represents the estimate of at time k given
observations up to, and including at time
k.
= represents the estimate of at time k
given observations up to, and including at
time k-1.
= represents error covariance of at time k
given observations up to, and including at
time k.
= represents error covariance of at time k
given observations up to, and including at
time k-1.
= represents error covariance of at time
k given observations up to, and including at time k-
1.
= is the control-input model which is applied to
the control vector uk
= is covariance matrix.
Thus the Kalman Filter is applied converted data
which smoothened data and improved the accuracy in GPS
positioning.
D. Modified Kalman Filter
The quality of the GPS data strongly depends on the GPS
signal condition, usually represented by the number of
satellites and PDOP values. When the condition of the GPS
signal does not reach the sufficient level of minimum
requirement, such as at least four satellites in view and
PDOP values less than or equal to eight, the measurement
errors are much greater than the above estimates. In
addition, the most important component of the Kalman filter
is the measurement error since the measurement error
determines how much random GPS random error should be
reduced. Thus modified the conventional discrete Kalman
filter by using two measurement errors based on the GPS
quality criteria, the number of satellites and PDOP values.
Researchers estimated the first measurement error in the
conditions of at least four satellites in view and PDOP
values less than or equal to eight and the second
measurement error from the other GPS signal conditions.
The Kalman filter is extensively used for state estimation for
linear systems under Gaussian noise. When non-Gaussian
L´evy noise is present, the conventional Kalman filter may
fail to be effective due to the fact that the non-Gaussian
L´evy noise may have infinite variance. A modified Kalman
filter for linear systems with non-Gaussian L´evy noise is
devised. It works effectively with reasonable computational
cost. Simulation results are presented to illustrate this non-
Gaussian filtering method.
The Time update and the Measurement Update
equations of Modified Kalman Filter are as given below:
Let kv~
represent the clipped version of the L´evy
measurement disturbance
kv , and let kz~
represent the corresponding clipped
observation. Thus
kkkk v~xHz~
In practice, since the measurement noise, kv is
unknown, we propose to clip the observation kz instead kv
of in a component-wise way by the following operation:
)xHC.sign(z~
j j
j
k
ji,
k
i
k
ji,
k
i
k zH
if
j
j
k
ji,
k
i
k
xH
z )≥C
=
i
kz if
j
j
k
ji,
k
i
k
xH
z <C
Where C is some positive threshold value,
i
kz and
i
kx represent the i-th components of the vectors z and x
respectively. Note that C is determined by the statistical
properties of the measurement noise kv . Replacing the
observation value z with its clipped value, we get
)xHz~(KxXˆ
kkkkkk
On substituting and calculating the above values we get
( ) (3.6)
Combining KF equations and the modifications
done we obtain the Modified Kalman Filter.
The results of GPS datum after conversion are
applied to the three filters discussed above for the reduction
of error introduced while conversion.
IV. RESULTS AND DISCUSSIONS
Table 1 shows the reduction of error in GPS datum
conversion. Table 2 &3 shows the comparision of Error
after applying Least Squares Algorithm, Kalman Filter,
Modified Kalman Filter respectively.
S.No
Error b/w UTM
receiver and Steven
Dutch equations
(meters)
Error b/w UTM
receiver and
Milenko.T.Ostojic
equations (meters)
Eastings Northings Eastings Northings
1. 133.58 312.92 28.98342 162.9848
2. 136.65 318.39 32.02523 159.9094
3. 135.38 320.24 32.9776 159.9496
4. 134.54 321.16 33.92997 159.9897
5. 135.61 321.72 31.9776 159.9496
6. 135.65 322.1 31.9776 159.9496
7. 136.14 327.36 34.92997 154.9897
8. 138.1 320.57 33.9776 161.9496
6. GPS Datum Conversion and Improvement in GPS Accuracy
(IJSRD/Vol. 2/Issue 08/2014/027)
All rights reserved by www.ijsrd.com 116
9. 136.83 319.73 30.02523 162.9094
10. 137.83 322.86 33.9776 159.9496
11. 135.84 320.96 31.9776 161.9496
12. 136.84 323.05 32.9776 159.9496
13. 137.66 323.13 31.02523 159.9094
14. 137.51 322.2 31.02523 160.9094
15. 137.54 322.25 33.9776 160.9496
. Table 1: Error between UTM receiver and Equations of
previous method and the improved method
S.No
Error b/w UTM
receiver and equations
after applying Least
squares Algorithm
Error between
Receiver and
Equations UTM after
Kalman Filtering
Easting
(meters)
Northings
(meters)
Easting
(meters)
Northing
(meters)
1. 28.34628 162.3028 28.98342 162.9848
2. 33.08795 161.047 32.0041 161.4631
3. 32.9776 159.9496 30.99036 160.9611
4. 32.23938 159.9667 30.22479 160.7183
5. 32.82289 159.961 31.37602 160.5639
6. 32.82289 159.961 31.47727 160.4605
7. 33.23938 154.9667 31.9784 155.3922
8. 33.9776 161.9496 33.9783 162.3356
9. 31.71582 162.9324 32.64006 163.2867
10. 33.1323 159.9381 33.67515 160.2517
11. 31.9776 161.9496 31.704 162.2229
12. 32.1323 159.9381 32.72816 160.1987
13. 31.87052 159.9209 33.50547 160.1749
14. 31.87052 160.9209 33.31355 161.1544
15. 33.1323 160.9381 33.36208 161.1394
Table 2: Comparision of error after applying Least Squares
Algorithm and Kalman Filter
S.No
Error b/w UTM
receiver and equations
after applying Kalman
Filter
Error between
Receiver and
Equations UTM after
Modified Kalman
Filtering
Easting
(meters)
Northings
(meters)
Easting
(meters)
Northing
(meters)
1. 28.98342 162.9848 28.98342 162.9848
2. 32.0041 161.4631 32.0043 161.4486
3. 30.99036 160.9611 31.48766 160.7007
4. 30.22479 160.7183 31.69102 160.3481
5. 31.37602 160.5639 32.3345 160.1489
6. 31.47727 160.4605 32.1561 160.0493
7. 31.9784 155.3922 33.51015 155.0202
8. 33.9783 162.3356 34.75479 161.9854
9. 32.64006 163.2867 32.15699 162.9528
10. 33.67515 160.2517 33.56165 159.9512
11. 31.704 162.2229 31.7682 161.9504
12. 32.72816 160.1987 32.87256 159.95
13. 33.50547 160.1749 33.19582 159.9403
14. 33.31355 161.1544 32.57635 160.9315
15. 33.36208 161.1394 33.08341 160.938
Table 3: Comparision of error after applying Kalman Filter
and Modified Kalman Filter
A. Simulation Results
The figures 2, 3, 4 show the simulation results before and
after applying the Least Squares algorithm, Kalman Filter
and Modified Kalman Filter respectively.
(a)
(b)
0 5 10 15 20 25 30 35
2.2369
2.2369
2.237
2.237
2.237
2.237
2.237
2.237
x 10
5
NUMBER OF READINGS
Eastingsinmeters
Eastings before appyling modified kalman filter
0 5 10 15 20 25 30 35
2.2369
2.2369
2.237
2.237
2.237
2.237
x 10
5
NUMBER OF READINGS
Eastingsinmeters
Eastings after appyling modified kalman filter
0 5 10 15 20 25 30 35
1.9269
1.927
1.927
1.927
1.927
1.927
1.927
x 10
6
NUMBER OF READINGS
Northingsinmeters
Northings before appyling modified kalman filter
0 5 10 15 20 25 30 35
1.9269
1.927
1.927
1.927
1.927
1.927
1.927
x 10
6
NUMBER OF READINGS
Northingsinmeters
Northings after appyling modified kalman filter
7. GPS Datum Conversion and Improvement in GPS Accuracy
(IJSRD/Vol. 2/Issue 08/2014/027)
All rights reserved by www.ijsrd.com 117
(c)
Fig. 4.1(a): UTM from equations before and after applying
least Squares Algorithm (b) UTM from equations Before
and after applying Kalman Filter (c) UTM From equation
before and after applying Modified Kalman Filter.
(a)
(b)
Fig. 4.2: shows the comparision of previous and latest
method.
(c)
Fig. 4.2: (a) & (b) comparision of Eastings and Northings error
of previous and latest method (c) & (d) Eastings and Northings
Error before and after LSA,KF & Modified KF
S.No
WGS-84 from
Receiver
WGS-84 obtained from
Modified Kalman
filtered UTM using
Milenko UTM to WGS-
84 conversion formulae
Latitude
(North)
Longitude
(East)
Latitude
(North)
Longitude
(East)
1. 17.4129 78.39886 17.40585 78.4000
2. 17.413 78.39886 17.40587 78.4000
3. 17.413 78.39888 17.40587 78.4000
4. 17.413 78.39891 17.40588 78.4000
5. 17.413 78.39888 17.40588 78.4000
6. 17.413 78.39888 17.40588 78.4000
7. 17.413 78.39891 17.40588 78.4000
8. 17.413 78.39888 17.40588 78.4000
9. 17.413 78.39886 17.40588 78.4000
10. 17.413 78.39888 17.40588 78.4000
11. 17.413 78.39888 17.40588 78.4000
12. 17.413 78.39888 17.40588 78.4000
13. 17.413 78.39886 17.40588 78.4000
14. 17.413 78.39886 17.40588 78.4000
15. 17.413 78.39888 17.40588 78.4000
Table 4: WGS-84 from receiver and Modified Kalman
Filtered UTM
V. CONCLUSIONS
n the latest method “G S datum conversion and mprovement
in G S accuracy” some of the errors involved in
0
50
100
150
1 4 7 10 13 16 19
Errorinmeters
No. of Readings
Comparision of Eastings error of
previous and latest methods
Eastings(met
ers) error in
previous
method
Eastings(met
ers) error in
latest method
0
100
200
300
400
1 4 7 10 13 16 19
Errorinmeters
No of readings
Comparision of Northings error of
previous and latest method
Northings(
meters) in
previous
method
25
27
29
31
33
35
37
39
1 5 9 13 17 21 25 29
ErrorinEastings(meters)
Number of Readings
Error in Eastings before and
after applying LSA,KF and
Modified KF
Eastings error
after conversion
Eastings error
after LSA
Eastings error
after KF
155
156
157
158
159
160
161
162
163
164
1 5 9 13 17 21 25 29
ErrorinNorthings(meters)
Number of Readings
Northings error before and after
LSA,KF and Modified KF
Northings
error after
conversion
Northings
error after
LSA
Northings
error after KF
Northings
error after
Modified Kf