Detecting Cognitive Radars and Meta-level Target Tracking

There are zillions of papers on sensor-level target tracking using filtering algorithms. We are interested in two deeper ideas that go beyond sensor-level tracking to the meta-level:

  1. Inverse Filtering and Inference: Given the response of a radar to our probe signal, how can we decide if the radar is cognitive? How can we estimate the radar’s accuracy? How can we reconstruct the tracked estimate of the enemy? How do we optimally probe the enemy’s radar to estimate its gain with minimum covariance? Such problems have significant relevance in electronic warfare. Our paper (listed below) addresses some of these issues. In a forthcoming paper we use ideas from micro-economics to give a novel methodology.
  2.  Meta-level tracking: Given track estimates, how do devise automated signal interpretation algorithms that assist a human operator to interpret the trajectory of a target? For example: How can one infer if a target is circling a building and therefore behaving suspiciously? A closed trajectory has long range dependencies – the beginning and end points coincide. Standard  Markovian state space models cannot capture the long range dependencies and spatial complexities of such trajectories We use natural language processing models such as  stochastic context free grammars and reciprocal stochastic processes to model the trajectories of targets.. Our meta-level target tracking goes beyond sensor-level tracking. We aim to infer the intent of the target – this constitutes the human-sensor interface (middleware).

Related Book and Papers

  1. V. Krishnamurthy, M. Rangaswamy, How to Calibrate your Adversary’s Capabilities? Inverse Filtering for Counter-Autonomous Systems, International Conference on Information Fusion (best paper award), 2019.
  2. Swedish collaborators + V. Krishnamurthy, Estimating Private Beliefs of Bayesian agents based on observed decisions, IEEE Control Systems Letters, July 2019.
  3. Swedish collaborators + V. Krishnamurthy, Inverse Filtering for Hidden Markov Models, NIPS 2017.
  4. V. Krishnamurthy, S. Gao, Syntactic Enhancement to VSIMM for Roadmap Based Anomalous Trajectory Detection: A Natural Language Processing Approach, IEEE Transactions Signal Processing, 2018. [simulation software]
  5. [Edited Book]. M. Mallick, V. Krishnamurthy and B. Vo,  Integrated Tracking Classification and Sensor Management, Wiley 2013.
  6. M. Fanaswala and V. Krishnamurthy,  Syntactic Models For Trajectory Constrained Track-Before-Detect, IEEE Transactions Signal Processing, Vol.62, No.23, pp.6130–6142, Dec. 2014.

  7. M. Fanaswala and  V. Krishnamurthy,  Spatio-Temporal Trajectory Models For Meta-Level Target Tracking, IEEE Aerospace and Electronic Systems Magazine,  Vol.30, No.1, pp.16–31, Jan. 2015.

  8. M. Fanaswalla, V. Krishnamurthy, Detection of anomalous trajectory patterns in target tracking using Stochastic Context Free Grammars and Reciprocal Processes, IEEE Journal Selected Topics in Signal Processing, Vo.7, No.1, pp.76–90, Jan., 2013.

  9. V. Krishnamurthy, M. Fanaswalla, Intent Inference via Syntactic Tracking, Digital Signal Processing, Vol2. No.5, pp.648–659, 2011.

  10. A. Wang, V. Krishnamurthy, B. Bhashyam, Intent Inference and Syntactic Tracking with GMTI Measurements, IEEE Transactions Aerospace and Electronic Systems, Vol.47, No.4, pp.2824–2843, October 2011.

  11. N. Visnevski, V Krishnamurthy, A. Wang and S. Haykin, Syntatic Modeling ad Signal Processing of Multifunction Radars: A Stochastic Context Free Grammar Approach. Proceedings of the IEEE, Special Issue on Large Scale Dynamic Systems, Vol.95, No.5, pp.1000–1025, May, 2007.