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Implementation of INS-GPS


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Implementation of INS-GPS

  1. 1. AH 2916Integrated Navigation Effectiveness of INS & GPS Integration from a Urban Perspective Ipsit Dash
  2. 2. Outline• Technological Milestones of INS and GPS Integration• Why INS-GPS integration?• INS-GPS Integration Architectures• Research Paper• Testing and Results• Conclusions• Literature Review 2
  3. 3. Milestones of INS and GPS Integration• Marine Gyrocompasses- End of 19th Century• 1930-Stand alone Gyrocompasses- incorporation of Damped Schuler Loop-• 1940-WW 2- Germans (V2 Guidance Systems) and British RAE simultaneously developed IN equipments for guiding their Missiles.• 1950- Schuler Tuned IN “Floated rate Integrating Gyro” developed by MIT, USA• 1960- Devlopment of Dynamically tuned Gyro• 1970- Tremendous advancement in IN field  Ring Laser Gyros ( RLG) and Hemispherical Resonator ( HRG)  Strapdown mechanism used in commercial flights- Boeing 757  Nuclear Magnetic Gyros (NMR) were developed  Fibre Optic Gyros (FOG)• 1980-RLG Strapdown System was used most significantly in Civil Aviation sectors. Gimballed IN systems continued to be used in Military sectors..• End of 20th Century- GPS !! A successor or a partner?? RLG Strap Down systems were improved and accuracy was improved and each unit contained a GPS receiver. 3
  4. 4. Why INS-GPS integration?? Most Navigation systems need to have- – Continuous and Reliable Navigation determination( Position and Orientation) – Acceptable Accuracy level and possibility of maintaining it over time GPS and INS symbiotic advantages- – Their Error Dynamics are totally different and uncorrelated. – GPS solves the problem of “calibrating” the instrument errors in a strapdown INS. – GPS provides a means of “in-flight” alignment for all INS. – The I.N. provides a seamless fill-in for GPS “outages” resulting from jamming, obscuration caused by manuvering etc. – The I.N. provides a means of smoothing the noisy velocity outputs from the GPS, and a continuous high bandwidth measurement of position and velocity. – In a tightly integrated system, the I.N. provides a means for narrowing the bandwidth of the GPS tracking loops, providing greater immunity to jamming. 4
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  6. 6. INS-GPS integration architectures Most Common-Loosely Coupled ( Decentralized Integration) Tightly Coupled ( Centralized Integration)•2 Kalman Filters •1 Kalman Filter•Advantages- •Advantages-•Simple in application •Can be used in Urban Areas ( poor Satellite•Robustness ( Sensors aiding each other) coverage)•Small Processing time •Raw and Predicted Pseudo Range and Doppler•Disadvantage- Measurement can lead to results.•Impossible to provide measurement update •Disadvantage-from GPS Filter when GPS cover is poor. •Increase in the State Vector Sizes lead to Large processing time Other methods- Uncoupled Integration Deep/Ultra- Tight Integration •Simple Method uses GPS solution if •Uses both GPS and INS solutions to available otherwise uses INS solution update and aid each other. •Low accuracy •Requires access to GPS Receiver firmware 6
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  9. 9. Research PaperFocus of the Research-• Use of Tightly coupled Integration for navigation in Urban Areas• Kalman Filter Smoothening algorithm to be used to post process thedata to obtain the position solution if its not available at the instant• High performance positioning can be achieved in post processing –Ideal for Low cost, High quality and Continuous Positioning. 9
  10. 10. Advantages of Post ProcessingApplications that can be benefitted/use Post processing to find positional solutions-• Surveying Application like Inventory Management• Terrestrial Georeferencing applications like Photogrammetric Surveying, Laser Scanning• Vehicle Performance Testing- Racing Cars or Product Testing• Surveillance- Commodity/ Vehicle Tracking• Road User- Charging where high accuracy is required and non availability of GPS service ( Urban Areas) 10
  11. 11. Kalman Filter Smoothing Algorithm• It is basically a post processing algorithm to find out the position solutions after all the data has been collected.• 3 basic types of Smoothening Filters-• Fixed Interval estimates the states at each of the points in a data set when all the data have been collected• Fixed Point used to estimate a specific point in a dataset• Fixed Lag can be applied in near real-time. 11
  12. 12. Fixed Interval Algorithm Advantage of Kalman filter smoothing during GPS outage (adapted from Gelb, 1977)When the forward and backward position estimates are computed, the INS positionerror quickly increases over time which is particularly so for systems that use a low costINS.When the Kalman filter smoothing algorithm is applied to the data, the error issignificantly reduced across the data outage interval. 12
  13. 13. For Near Real- Time Cases• Rauch- Tung- Striebel (RTS) algorithm can be used in near- real time by running the smoothing algorithm on short periods of data throughout during the data collection. Ex- For example, after every significant GPS outage, the algorithm could be applied once GPS availability returns.• Advantages- The RTS algorithm greatly reduces the computational effort required for Kalman filter smoothing since it only requires the full Kalman filter to be implemented in the forward direction.• Limitations- when carrier phase ambiguity states are modelled, the INS will not have the advantage of using fixed ambiguity GPS measurements that may have been resolved if a full reverse filter implementation were to have been implemented. 13
  14. 14. Implementation- Field Trial• A test route was devised to incorporate a range of conditions from relatively clear GPS conditions, to semi obstructed surroundings in suburban locations, to deep urban conditions where there is a significantly restricted sky view. The route was driven twice ( approx 25 mins).• Gyro Biasing Estimate- 1 min• 5 minute Alignment Period to find the Initial gyro and accelerometer biases• Initial Heading estimate was obtained from velocity of vehicle used.• Roll and Pitch initialized to 0.• IMU used- Crossbow AHRS400CA ( < $3000)• Differential GPS receiver used- Novatel OEM4 GPS (L1 Pseudo range and Doppler measurements were used. It was used with Leica GPS530 reference receiver.• The lever arm separation between the IMU and GPS antenna was calculated by using a total station.• A Novatel OEM4 GPS receiver integrated with a high accuracy Ring Laser Gyro Honeywell CIMU was also installed in the vehicle to act as a reference for the experiment. It was integrated using Loosely coupled algorithm.• The Processing was done using software – KinPos and Applanix 14
  15. 15. Vehicle trajectory GPS IMUs Total station survey for the estimation of lever arm separation between GPS receiver and IMUsSatellite availability during Nottingham trial 15
  16. 16. Results 16
  17. 17. Kalman Filter Smoothening ResultsHorizontal position error of GPS and low Typical example of positioning errorcost INS system during Nottingham trial during restricted satellite availability 17
  18. 18. Conclusions• It is clear from the results discussed in this paper that GPS and low cost INS integrated systems can meet the performance levels required for a number of applications, particularly in Urban Areas.• The benefit of post-processing the data was shown to be substantial. This can be utilised in many navigational purposes with limited GPS availability. 18
  19. 19. Literature Review• Michael Cramer, GPS/INS Integration, University of Stuttgart- Photogrammetric Week 97- D.Fritsch & D.Hobbie, Eds 1997• Vikas Kumar N., Prof. K. Sudhakar, Integration of Inertial Navigation System and Global Positioning System Using Kalman Filtering, DEPARTMENT OF AEROSPACE ENGINEERING INDIAN INSTITUTE OF TECHNOLOGY, BOMBAY 2004• A. D. KING., Inertial Navigation – Forty Years of Evolution, Marconi Electronic Systems Ltd.• Alison K. Brown, TEST RESULTS OF A GPS/INERTIAL NAVIGATION SYSTEM USING A LOW COST MEMS IMU NAVSYS Corporation, 14960 Woodcarver Road, Colorado Springs, CO 80921 USA,• George Schmidt, INS/GPS Integration Architectures, Sponsored by the NATO Research and Technology Organization• Milan Horemuž, Integrated Navigation Compendium, Division of Geodesy and Geoinformatics, Royal Institute of Technology, Sweden 2006 19
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