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

Abstract

Modern cognitive radar networks incorporating intelligent and cognitive support-modules can actively adjust the radar-target geometry and optimally select a subset of radars to track the target of interest. Based on the theories of dynamic graphical models (DGM) and recursive Bayesian state estimation (RBSE), we propose a framework for single target tracking in mobile and cooperative radar networks, jointly considering path planning and radar selection. We formulate the tracking procedure as two iterative steps: (i) solving a combinatorial problem based on the expected cross-entropy measure to select the optimal subset of radars and their locations, and (ii) tracking the target using RBSE technique. We simulate the proposed framework using an illustrative example in 2-D space and demonstrate the tracking performance.

Authors:
 [1];  [2]; ORCiD logo [3];  [4];  [1]
  1. Washington University, St. Louis
  2. University of Pittsburgh
  3. ORNL
  4. Northeastern University, Boston
Publication Date:
Research Org.:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1435261
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: Asilomar Conference on Signals, Systems, and Computers (ACSSC 2017) - Pacific Grove, California, United States of America - 10/29/2017 8:00:00 AM-11/1/2017 8:00:00 AM
Country of Publication:
United States
Language:
English

Citation Formats

Xiang, Yijian, Akcakaya, Murat, Sen, Satyabrata, Erdogmus, Deniz, and Nehorai, Arye. Target tracking via recursive Bayesian state estimation in radar networks. United States: N. p., 2017. Web. doi:10.1109/ACSSC.2017.8335475.
Xiang, Yijian, Akcakaya, Murat, Sen, Satyabrata, Erdogmus, Deniz, & Nehorai, Arye. Target tracking via recursive Bayesian state estimation in radar networks. United States. https://doi.org/10.1109/ACSSC.2017.8335475
Xiang, Yijian, Akcakaya, Murat, Sen, Satyabrata, Erdogmus, Deniz, and Nehorai, Arye. 2017. "Target tracking via recursive Bayesian state estimation in radar networks". United States. https://doi.org/10.1109/ACSSC.2017.8335475. https://www.osti.gov/servlets/purl/1435261.
@article{osti_1435261,
title = {Target tracking via recursive Bayesian state estimation in radar networks},
author = {Xiang, Yijian and Akcakaya, Murat and Sen, Satyabrata and Erdogmus, Deniz and Nehorai, Arye},
abstractNote = {Modern cognitive radar networks incorporating intelligent and cognitive support-modules can actively adjust the radar-target geometry and optimally select a subset of radars to track the target of interest. Based on the theories of dynamic graphical models (DGM) and recursive Bayesian state estimation (RBSE), we propose a framework for single target tracking in mobile and cooperative radar networks, jointly considering path planning and radar selection. We formulate the tracking procedure as two iterative steps: (i) solving a combinatorial problem based on the expected cross-entropy measure to select the optimal subset of radars and their locations, and (ii) tracking the target using RBSE technique. We simulate the proposed framework using an illustrative example in 2-D space and demonstrate the tracking performance.},
doi = {10.1109/ACSSC.2017.8335475},
url = {https://www.osti.gov/biblio/1435261}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Sun Oct 01 00:00:00 EDT 2017},
month = {Sun Oct 01 00:00:00 EDT 2017}
}

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