Radar was invented in the early 1900s and applied during World War II to detect aircraft. The basic principles of radar involve transmitting electromagnetic signals that are reflected off targets and detected. A typical radar system includes a transmitter, antenna, receiver, and display. The radar range equation relates key variables such as transmitted power, wavelength, target radar cross-section, and system losses to the maximum detectable range. Integration of multiple radar returns can improve the signal-to-noise ratio and increase detection range.
Tutorial Content
This tutorial provides a broad-based discussion of radar system, covering the following topics:
-Introduction to Radars in Military and Commercial Applications
-Radar System Block Diagram
-Radar Antennas (slotted waveguide array, planar array), Transmitter (magnetron, solid-state), Receiver, Pedestal and Radome
-Plot Extraction, Tracking Algorithms and Display
-Radar Range Equation, Detection Performance
-Wave Propagation and Radar Cross Section
-Emerging and Advanced Radar Systems (phased-array, multi-beam, multi-mode, FMCW, solid-state)
In the discussion, practical systems, technical specifications and data will be used to enhance learning.In addition, simulation results will also be used to present findings.
The objective of the tutorial session is to equip participants with solid understanding of radar systems for system level applications and prepare them for advanced and professional radar courses, projects and research.
This tutorial is designed and developed based on the following references:
[1] G. W. Stimson, Introduction to Airborne Radar Second Edition, Scitech Publishing, 1998.
[2] L. V. Blake, A Guide to Basic Pulse-Radar Maximum-Range Calculation, NRL Report 6930, 1969.
[3] K. H. Lee, Radar Systems for Nanyang Technological University, TBSS, 2014.
Tutorial Content
This tutorial provides a broad-based discussion of radar system, covering the following topics:
-Introduction to Radars in Military and Commercial Applications
-Radar System Block Diagram
-Radar Antennas (slotted waveguide array, planar array), Transmitter (magnetron, solid-state), Receiver, Pedestal and Radome
-Plot Extraction, Tracking Algorithms and Display
-Radar Range Equation, Detection Performance
-Wave Propagation and Radar Cross Section
-Emerging and Advanced Radar Systems (phased-array, multi-beam, multi-mode, FMCW, solid-state)
In the discussion, practical systems, technical specifications and data will be used to enhance learning.In addition, simulation results will also be used to present findings.
The objective of the tutorial session is to equip participants with solid understanding of radar systems for system level applications and prepare them for advanced and professional radar courses, projects and research.
This tutorial is designed and developed based on the following references:
[1] G. W. Stimson, Introduction to Airborne Radar Second Edition, Scitech Publishing, 1998.
[2] L. V. Blake, A Guide to Basic Pulse-Radar Maximum-Range Calculation, NRL Report 6930, 1969.
[3] K. H. Lee, Radar Systems for Nanyang Technological University, TBSS, 2014.
RADAR - RAdio Detection And Ranging
This is the Part 1 of 2 of RADAR Introduction.
For comments please contact me at solo.hermelin@gmail.com.
For more presentation on different subjects visit my website at http://www.solohermelin.com.
Part of the Figures were not properly downloaded. I recommend viewing the presentation on my website under RADAR Folder.
10 range and doppler measurements in radar systemsSolo Hermelin
Present method of Range and Doppler measurement in a RADAR system.
For comments please contact me at solo.hermelin@gmail.com.
For more presentations on different subjects visit my website at http://www.solohermelin.com.
Recommend to view this presentation on my website in power point.
Working Processes Of Radar
History – Before Radar
Principle Of Operation
Radio Detection And Ranging
Radar Functions
Radar Bands And Usage
Terminology Of Radar Systems
Radar Range Equation
Types Of Radar
Pulse RADAR
Duplexer Using Pin Switches
Doppler Effect
Principle Of Continuous Wave Radar
Principles Of MTI RADAR
Different Types Of RADAR & It’s Applications
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Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
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Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
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Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
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Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
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We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
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Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
19. Radar Antennas Radars which are required to determine the directions as well as the distances of targets require antenna patterns which have narrow beamwidths e.g. The narrower the beamwidth, the more precise the angle Fortunately, a narrow beamwidth also gives a high Gain which is desirable as we shall see.
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21. Radar Antennas Phased Arrays One of the big disadvantages of the parabolic antennas is that they have to be physically rotated in order to cover their area of responsibility. Also, military uses sometimes require the beam to be moved quickly from one direction to another. For these applications Phased Array antennas are used
62. Calculation of Minimum Signal to Noise Ratio Now we have a relationship between False alarm time and the threshold to noise ratio This can be used to set the Threshold level
63. Calculation of Minimum Signal to Noise Ratio Now we add a signal of amplitude A and the pdf becomes Ricean. i.e. a Rice distribution This is actually a Rayleigh distribution distorted by the presence of a sine wave Where I 0 is a modified Bessel function of zero order
65. Calculation of Minimum Signal to Noise Ratio From this graph, the minimum signal to noise ratio can be derived from: a. the probability of detection b. the probability of false alarm
66. Integration of Radar Pulses Note that the previous calculation for signal to noise ratio is based on the detection of a single pulse In practice a target produces several pulses each time the antenna beam sweeps through its position Thus it is possible to enhance the signal to noise ratio by integrating (summing ) the pulse outputs. Note that integration is equivalent to low pass filtering. The more samples integrated, the narrower the bandwidth and the lower the noise power
67. Integration of Radar Pulses Note: The antenna beam width n b is arbitrarily defined as the angle between the points at which the pattern is 3dB less than the maximum 3dB Beam Width θ B If the antenna is rotating at a speed of θ S º/s and the Pulse repetition frequency is f p the number of pulses on target is n B = θ B f p / θ S or if rotation rate is given in rpm (ω m ) n B = θ B f p / 6 ω m
68. Integration of Radar Pulses integration before detection is called predetection or coherent detection integration after detection is called post detection or noncoherent detection If predetection is used SNR integrated = n SNR 1 If postdetection is used, SNR integrated n SNR 1 due to losses in the detector
69. Integration of Radar Pulses Predetection integration is difficult because it requires maintaining the phase of the pulse returns Postdetection is relatively easy especially using digital processing techniques by which digitized versions of all returns can be recorded and manipulated
70. Integration of Radar Pulses The reduction in required Signal to Noise Ratio achieved by integration can be expressed in several ways: Integration Efficiency: Note that E i (n) is less than 1 (except for predetection) Where (S/N) 1 is the signal to noise ratio required to produce the required P d for one pulse and And (S/N) n is the signal to noise ratio required to produce the required P d for n pulses
71. Integration of Radar Pulses The improvement in SNR where n pulses are integrated is called the integration improvement factor I i (n) Note that I i (n) is less than n Another expression is the equivalent number of pulses n eq
73. Integration of Radar Pulses False Alarm Number Note the parameter n f in the graph This is called the false alarm number and is the average number of “decisions” between false alarms Decisions are considered as the discrete points at which a target may be detected unambiguously Recall that the resolution of a radar is half the pulse width multiplied by the speed of light τ τ τ
74. Integration of Radar Pulses False Alarm Number Thus the total number of unambiguous targets for each transmitted pulse is T/ τ where T is the pulse repetition period (1/f P ) We multiply this by the number of pulses per second (f P ) to get the number of decisions per second Finally we multiply by the False alarm rate (T fa ) to get the number of decisions per false alarm. n f = [ T/ τ][f P ][T fa ]
75. Integration of Radar Pulses False Alarm Number But T x f P =1 and τ 1/B where B is the IF bandwidth so n f T fa B 1/P fa n f = [ T/ τ][f P ][T fa ]
76. Integration of Radar Pulses Effect on Radar Range Equation Range Equation with integration Expressed in terms of SNR for 1 pulse
95. Radar Cross Section The effect of Cross section fluctuation on required Signal to Noise
96. Radar Cross Section Calculating the Effect of fluctuating cross section on Radar Range Additional SNR
97. Radar Cross Section Calculating the Effect of fluctuating cross section on Radar Range Modified Integration Efficiency
98. Radar Cross Section Calculating the Effect of fluctuating cross section on Radar Range To incorporate the varying radar cross section into the Radar Range Equation: 1. Find S/N from Fig 2.7 using required P d and P fa 2. From Fig 2.23, find the correction factor for the Swerling number given, calculate (S/N) 1 3. If n pulses are integrated, use Fig 2.24 to find the appropriate I i (n) 4. Substitute the (S/N) 1 and I i (n) into the equation
99. Radar Cross Section Calculating the Effect of fluctuating cross section on Radar Range Example: P d = 90% P fa = 10 -4 Antenna beam width: 2 º Antenna rotation rate: 6 rpm f p =400Hz Target: Swerling II
100. Radar Cross Section Calculating the Effect of fluctuating cross section on Radar Range (S/N) 1 =12dB additional (S/N) =8dB new (S/N) 1 =20dB
101. Radar Cross Section Calculating the Effect of fluctuating cross section on Radar Range number of pulses integrated n= θ x f p /6xω = 2x400/36 = 22.2 I n (n)= 18 dB
102. Radar Cross Section Calculating the Effect of fluctuating cross section on Radar Range Note that the Swerling Cases are only very crude approximations Swerling himself has since modified his ideas on this and has extended his models to include a range of distributions based on the Chi-square (or Gamma) distribution
103. Radar Cross Section Radar Cross Section The objective is to obtain the specified probability of detection with the minimum Transmitter power This is because the size, cost and development time for a radar are a function of the maximum transmitter power Thus it is important to develop a correct model for the expected targets
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107. Transmitter Power For radars which do not use pulse waveforms the average energy per repetition is used:
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110. Antenna Parameters Gain Definition: Note that since the total power radiated can not be more than the power received from the transmitter, G( θ,φ)d θ d φ < 1 Therefore, if the gain is greater than 1 in one direction it is less than one in others.
114. Antennas Pencil beams are not good for searching large areas of sky. Search radars usually use fan beams which are narrow in azimuth and wide in elevation The elevation pattern is normally designed to be of “cosecant squared” pattern which gives the characteristic that a target at constant altitude will give a constant signal level.
115. Antennas φ 0 <φ<φ m substituting in radar range equation Note: There is an error in the notes
117. Antennas Beamwidth vs Scan Rate This tradeoff in the radar design is between a. being able to track the target which implies looking at it often and b. detecting the target which implies integrating a lot of pulses at each look Note: increasing the PRF decreases the unambiguous range
118. Radar Cross Section Questions: 1. Design a test to measure the Radar Cross Section of an object 2. A corner cube reflector reflects all of the energy that hits it back towards the radar. Assuming a physical area of 1 m 2 and a “beam width” of the reflected energy to be equal to the beam width of the radar antenna, What is the RCS of the reflector?
119. Losses Controllable losses fall into three categories: a. Antenna Beam shape b. Plumbing Loss b. Collapsing Loss
120. Losses Beam Shape Loss During the previous discussions it was assumed that the signal strength was the same for all pulses while the antenna beam was on the target. This, of course is no true. The beamwidth is defined as being between the 3 dB points and so the signal strength varies by 3 dB as it passes the target
121. Losses Beam Shape Loss The shape of the beam between the 3 dB points is assumed to be Gaussian i.e. where θ B is the half power beam width and the amplitude of the maximum pulse is 1.
122. Losses Beam Shape Loss θ =k θ B /(n B -1) Two way beam shape: S 4 =exp(-5.55( θ 2 /θ B 2 )) S 4 =exp(-5.55( k/(n B -1)) 2 ) 1 The sum of the power of the four RH pulses is θ B θ B /(n B -1) 1 2 3 4 k
123. Losses Beam Shape Loss 1 The sum of the power of the ALL pulses is The ratio of the power in n equal to the power in the actual pulses is NOTE: Error in Notes θ B θ B /(n B -1) 1 2 3 4 k
124. Losses Plumbing Loss Almost all of the signal path in a radar is implemented by waveguide Exception: UHF frequencies where waveguide size becomes unwieldy. This is because a. waveguide can sustain much higher power levels than coaxial cable. (and can be pressurized) b. Losses in waveguide are much lower than in coaxial cable
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128. Losses Plumbing Loss Note that losses in common transmit/receive path must be doubled
129. Losses Collapsing Loss If a radar collects data in more dimensions than can be used, it is possible for noise to be included in the measurement in the dimension “collapsed” or discarded. n n n n e.g. if a radar measures elevation as well as range and azimuth, it will store target elevation information in an vector for each range/azimuth point. If only range and azimuth are to be displayed, the elevation cells are “collapsed” and thus many noise measurements are added with the actual target information n s+n n n n n n n n n n n n s+n s+n s+n n n n n n n
131. Losses Collapsing Loss n n n n Example: 10 cells with signal+noise, 30 cells with noise P d =0.9 n fa =10 -8 3 4 2.1 1.4 L i (30)=3.5dB L i (10)=1.7dB L C (30,10)=1.8dB
132. Surveillance Radar n n n n Radar discussed so far is called a searchlight radar which dwells on a target for n pulses. With the additional constraint of searching a specified volume of space in a specified time the radar is called a search or surveillance radar. Ω is the (solid) angular region to be searched in scan time t s then where t 0 is the time on target n/f p Ω 0 = the solid angle beamwidth of the antenna θ A θ E