Target Detection via Cognitive Radars Using Change-Point Detection, Learning, and Adaptation

Xiang, Yijian; Akcakaya, Murat; Sen, Satyabrata; Nehorai, Arye

doi:10.1007/s00034-020-01465-z

Title: Target Detection via Cognitive Radars Using Change-Point Detection, Learning, and Adaptation

Journal Article · Thu Jun 11 00:00:00 EDT 2020 · Circuits, Systems, and Signal Processing

DOI:https://doi.org/10.1007/s00034-020-01465-z· OSTI ID:1787561

Xiang, Yijian ^[1]; Akcakaya, Murat ^[2]; Sen, Satyabrata ^[3];

^[1]

Washington Univ., St. Louis, MO (United States). Dept. of Electrical and Systems Engineering
Univ. of Pittsburgh, PA (United States). Dept. of Electrical and Computer Engineering
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Computational Sciences and Engineering Division

Many radar detection algorithms that assume a stationary environment (clutter) have been proposed and analyzed over the years. However, in practice, changes in the nonstationary environment can perturb the parameters of the clutter distribution, or even alter the clutter distribution family, which can greatly deteriorate the target detection capability. To avoid such potential performance degradation, cognitive radar systems are envisioned which are required to rapidly realize the nonstationarity, accurately learn the new characteristics of the environments, and adaptively update the detector. In this paper, aiming to develop a fully cognitive radar for target detection in nonstationary environments, we propose a unifying framework that integrates (i) change-point detection of clutter distributions by using a data-driven cumulative sum (CUSUM) algorithm and its extended version, (ii) learning/identification of clutter distribution by applying sparse theory and kernel density estimation methods, and (iii) adaptive target detection by automatically modifying the likelihood-ratio test and corresponding detection threshold. Further, with extensive numerical examples, we demonstrate the achieved improvements in detection performance due to the proposed framework in comparison to a nonadaptive case, an adaptive matched filter (AMF) method, and the clairvoyant case. Herein, we also use Wilcoxon rank-sum tests to evaluate the statistical significance of the performance improvements

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Research Organization:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES); US Air Force Office of Scientific Research (AFOSR)

Grant/Contract Number:: AC05-00OR22725; FA9550-16-1-0386

OSTI ID:: 1787561

Journal Information:: Circuits, Systems, and Signal Processing, Vol. 40, Issue 1; ISSN 0278-081X

Publisher:: Springer NatureCopyright Statement

Country of Publication:: United States

Language:: English

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