Distinguished Lecture: Angle-only tracking in three dimensions

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AESS Distinguished Lecture

Abstract:  The angle-only filtering (AOF) problem in 3D arises in many real-world passive tracking problems. These problems include passive ranging using an infrared search and track (IRST) sensor, passive sonar, passive radar in the presence of jamming, and satellite-to-satellite passive tracking. We consider estimating the state of a target using noisy bearing and elevation measurements. We first describe the dynamic models of the target and senor and different coordinate systems. The filter initialization, which forms an important part of the filtering algorithm, is explained next. Several nonlinear filters including the extended Kalman filter (EKF), unscented Kalman filter (EKF), cubature Kalman filter (CKF), and particle filter (PF) are used for performance evaluation. We also compare the performance of these nonlinear filters with the posterior Cramér-Rao lower bound (PCRLB). Finally, we discuss other topics in the AOF problem not covered in this presentation.


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  • Carleton University
  • 1125 Colonel By Drive
  • Ottawa, Ontario
  • Canada K1S 5B6
  • Building: Mackenzie Bldg (ME)
  • Room Number: 4463
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  • Starts 12 October 2025 04:00 PM UTC
  • Ends 14 October 2025 07:30 PM UTC
  • No Admission Charge

  Speakers

Mahendra Mallick

Topic:

Angle-only tracking in three dimensions

The angle-only filtering (AOF) problem in 3D arises in many real-world passive tracking problems. These problems include passive ranging using an infrared search and track (IRST) sensor, passive sonar, passive radar in the presence of jamming, and satellite-to-satellite passive tracking. We consider estimating the state of a target using noisy bearing and elevation measurements. We first describe the dynamic models of the target and senor and different coordinate systems. The filter initialization, which forms an important part of the filtering algorithm, is explained next. Several nonlinear filters including the extended Kalman filter (EKF), unscented Kalman filter (EKF), cubature Kalman filter (CKF), and particle filter (PF) are used for performance evaluation. We also compare the performance of these nonlinear filters with the posterior Cramér-Rao lower bound (PCRLB). Finally, we discuss other topics in the AOF problem not covered in this presentation.

Biography:

Mahendra Mallick (Life Senior Member, IEEE) received an M.S. degree in computer science from The Johns Hopkins University, Baltimore, MD, USA, in 1987, and a Ph.D. degree in quantum solid-state theory from the State University of New York, Albany, NY, USA, in 1981. He is currently an independent Consultant in Cambria, CA, USA. He is a coeditor and a co-author of the book entitled Integrated Tracking, Classification, and Sensor Management: Theory and Applications (New York, NY, USA:Wiley/IEEE, 2012). His research interests include nonlinear filtering, multisensor multitarget tracking, multiple hypothesis tracking, random finite-set-based multitarget tracking, space object tracking, and distributed fusion. Dr. Mallick was the Associate Editor-in-Chief of the online journal of the International Society of Information Fusion (ISIF) in 2008–2009. He was a member of the Board of Directors of the ISIF in 2008–2010. He was the Lead Guest Editor of the special issue on Multitarget Tracking in the IEEE Journal of Selected Topics in Signal Processing in June 2013. He was the Lead Guest Editor of the Sensors 2021-2022 Special Issue on Advances in Angle-Only Filtering and Tracking in Two and Three Dimensions. He was an Associate Editor for target tracking and multisensor systems of the IEEE Transactions on Aerospace and Electronic Systems, 2018-2024

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