Target tracking via recursive Bayesian state estimation in cognitive radar networks

Journal Article · 26 September 2018 · Signal Processing

DOI: https://doi.org/10.1016/j.sigpro.2018.09.035 · OSTI ID:1476417

Xiang, Yijian ^[1]; Akcakaya, Murat ^[2]; Sen, Satyabrata ^[3]; Erdogmus, Deniz ^[4]; Nehorai, Arye ^[1]

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1. Washington Univ., St. Louis, MO (United States)
2. Univ. of Pittsburgh, PA (United States)
3. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
4. Northeastern Univ., Boston, MA (United States)

To cope with complicated environments and stealthier targets, incorporating intelligence and cognition cycles into target tracking is of great importance in modern sensor network management. With remarkable advances in sensor techniques and deployable platforms, a sensing system has freedom to select a subset of available radars, plan their trajectories, and transmit designed waveforms. In this paper, we propose a general framework for single target tracking in cognitive networks of radars, including consideration of waveform design, path planning, and radar selection, which are separately but not jointly taken into account in existing work. The tracking procedure, built on the theories of dynamic graphical models (DGM) and recursive Bayesian state estimation (RBSE), is formulated as two iterative steps: (i) solving a combinatorial optimization problem to select the optimal subset of radars, waveforms, and locations for the next tracking instant, and (ii) acquiring the recursive Bayesian state estimation to accurately track the target. Further, an illustrative example introduces a specific scenario in 2-D space. Simulation results based on the scenario demonstrate that the proposed framework can accurately track the target under the management of the network of radars.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 1476417
• Alternate ID(s): OSTI ID: 1702557
• Journal Information: Signal Processing, Vol. 155, Issue C; ISSN 0165-1684
• Country of Publication: United States
• Language: English