Cognitive radar; colored interference; extended Kalman filter; intelligent target; inverse cognition; Software; Signal Processing; Electrical and Electronic Engineering
Abstract :
[en] A scenario with multiple radars connected to a fusion centre and tracking a target endowed with cognitive abilities is considered. The aim of the target is to degrade the performance of the radar network using its cognitive abilities. In the embodiment considered in this paper, the target injects interference that perturbs the measurements at the different radars. The injected interference is designed to maximize the trace of the error covariance matrix in each instance of the extended Kalman filter iterations used at the fusion centre. The optimal interference in such a setting is formulated as a convex problem and its structure reveals a low-rank correlated structure unlike the intuitive additive white noise. Relation to water-filling is drawn and the impact of such an interference is subsequently analysed using numerical simulations.
Disciplines :
Electrical & electronics engineering
Author, co-author :
BHATIA, Jyoti ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SPARC
RAJPUT, Kunwar ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SPARC
OTTERSTEN, Björn ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > PI Ottersten
MYSORE RAMA RAO, Bhavani Shankar ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SPARC
External co-authors :
no
Language :
English
Title :
Intelligent Target Maneuverability in Presence of Tracking with Multiple Radars
Publication date :
2025
Event name :
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Event place :
Hyderabad, Ind
Event date :
06-04-2025 => 11-04-2025
Main work title :
2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025 - Proceedings
Editor :
Rao, Bhaskar D
Publisher :
Institute of Electrical and Electronics Engineers Inc.
S. Haykin, “Cognitive radar: a way of the future, ” IEEE Signal Processing Magazine, vol. 23, no. 1, pp. 30-40, 2006.
V. Krishnamurthy and D. V. Djonin, “Optimal threshold policies for multivariate pomdps in radar resource management, ” IEEE Transactions on Signal Processing, vol. 57, no. 10, pp. 3954-3969, 2009.
A. E. Mitchell, G. E. Smith, K. L. Bell, and M. Rangaswamy, “Single target tracking with distributed cognitive radar, ” in 2017 IEEE Radar Conference (RadarConf), 2017, pp. 0285-0288.
J. Yan, B. Jiu, H. Liu, B. Chen, and Z. Bao, “Prior knowledge-based simultaneous multibeam power allocation algorithm for cognitive multiple targets tracking in clutter, ” IEEE Transactions on Signal Processing, vol. 63, no. 2, pp. 512-527, 2015.
Z. Li, J. Xie, W. Liu, H. Zhang, and H. Xiang, “Joint strategy of power and bandwidth allocation for multiple maneuvering target tracking in cognitive MIMO radar with collocated antennas, ” IEEE Transactions on Vehicular Technology, vol. 72, no. 1, pp. 190-204, 2023.
P. Chavali and A. Nehorai, “Scheduling and power allocation in a cognitive radar network for multiple-target tracking, ” IEEE Transactions on Signal Processing, vol. 60, no. 2, pp. 715-729, 2012.
W. W. Howard, A. F. Martone, and R. M. Buehrer, “Timely target tracking: Distributed updating in cognitive radar networks, ” IEEE Transactions on Radar Systems, vol. 2, pp. 318-332, 2024.
R. Gui, W.-Q. Wang, Y. Pan, and J. Xu, “Cognitive target tracking via angle-range-doppler estimation with transmit subaperturing FDA radar, ” IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 1, pp. 76-89, 2018.
K. V. Mishra, M. R. B. Shankar, and B. Ottersten, “Toward metacognitive radars: Concept and applications, ” in 2020 IEEE International Radar Conference (RADAR), 2020, pp. 77-82.
T. D. Ridder, A. F. Martone, B. H. Kirk, and R. M. Narayanan, “Multiple-criteria operational reliability performance metric of a metacognitive tracking radar, ” IEEE Transactions on Aerospace and Electronic Systems, vol. 59, no. 4, pp. 4689-4699, 2023.
W. W. Howard and R. M. Buehrer, “Hybrid cognition for target tracking in cognitive radar networks, ” IEEE Transactions on Radar Systems, vol. 1, pp. 118-131, 2023.
K. V. Mishra, M. R. Bhavani Shankar, and M. Rangaswamy, Next-Generation Cognitive Radar Systems. IET Press, 2023.
V. Krishnamurthy and M. Rangaswamy, “How to calibrate your adversary's capabilities? inverse filtering for counter-autonomous systems, ” IEEE Transactions on Signal Processing, vol. 67, no. 24, pp. 6511-6525, 2019.
V. Krishnamurthy, D. Angley, R. Evans, and B. Moran, “Identifying cognitive radars-inverse reinforcement learning using revealed preferences, ” IEEE Transactions on Signal Processing, vol. 68, pp. 4529-4542, 2020.
V. Krishnamurthy, K. Pattanayak, S. Gogineni, B. Kang, and M. Rangaswamy, “Adversarial radar inference: Inverse tracking, identifying cognition, and designing smart interference, ” IEEE Transactions on Aerospace and Electronic Systems, vol. 57, no. 4, pp. 2067-2081, 2021.
K. Pattanayak, V. Krishnamurthy, and C. M. Berry, “Metacognitive radar: Masking cognition from an inverse reinforcement learner, ” IEEE Transactions on Aerospace and Electronic Systems, vol. 59, no. 6, pp. 8826-8844, 2023.
K. V. Mishra, M. R. B. Shankar, and B. Ottersten, “Toward metacognitive radars: Concept and applications, ” in 2020 IEEE International Radar Conference (RADAR), 2020, pp. 77-82.
D. Palomar, J. Cioffi, and M. Lagunas, “Joint tx-rx beam-forming design for multicarrier mimo channels: a unified framework for convex optimization, ” IEEE Transactions on Signal Processing, vol. 51, no. 9, pp. 2381-2401, 2003.