October 2017

Conference Paper

Target tracking via recursive Bayesian state estimation in radar networks

By:
Xiang, Yijian; Akcakaya, Murat; Sen, Satyabrata ; Erdogmus, Deniz; Nehorai, Arye
Page Number:
880-884
Volume:
1
Issue Number:
1
Book Title:
2017 51st Asilomar Conference on Signals, Systems, and Computers
Publication Date:
October 2017
Conference Name:
Asilomar Conference on Signals, Systems, and Computers (ACSSC 2017)
Conference Location:
Pacific Grove, California, United States of America
Conference Sponsor:
IEEE
View DOI Listing:
https://doi.org/10.1109/ACSSC.2017.8335475

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.