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Title: Target tracking via recursive Bayesian state estimation in cognitive radar networks

Abstract

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.

Authors:
 [1];  [2];  [3];  [4];  [1]
  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)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1476417
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Signal Processing
Additional Journal Information:
Journal Volume: 155; Journal Issue: C; Journal ID: ISSN 0165-1684
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION

Citation Formats

Xiang, Yijian, Akcakaya, Murat, Sen, Satyabrata, Erdogmus, Deniz, and Nehorai, Arye. Target tracking via recursive Bayesian state estimation in cognitive radar networks. United States: N. p., 2018. Web. doi:10.1016/j.sigpro.2018.09.035.
Xiang, Yijian, Akcakaya, Murat, Sen, Satyabrata, Erdogmus, Deniz, & Nehorai, Arye. Target tracking via recursive Bayesian state estimation in cognitive radar networks. United States. doi:10.1016/j.sigpro.2018.09.035.
Xiang, Yijian, Akcakaya, Murat, Sen, Satyabrata, Erdogmus, Deniz, and Nehorai, Arye. Wed . "Target tracking via recursive Bayesian state estimation in cognitive radar networks". United States. doi:10.1016/j.sigpro.2018.09.035. https://www.osti.gov/servlets/purl/1476417.
@article{osti_1476417,
title = {Target tracking via recursive Bayesian state estimation in cognitive radar networks},
author = {Xiang, Yijian and Akcakaya, Murat and Sen, Satyabrata and Erdogmus, Deniz and Nehorai, Arye},
abstractNote = {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.},
doi = {10.1016/j.sigpro.2018.09.035},
journal = {Signal Processing},
number = C,
volume = 155,
place = {United States},
year = {2018},
month = {9}
}

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