TY - GEN
T1 - Optimal Target Detection for Random Channel Matrix-Based Cognitive Radar/Sonar
AU - Ali, Touseef
AU - Richmond, Christ D.
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/5/7
Y1 - 2021/5/7
N2 - Conventional techniques for characterizing clutter depend on covariance-based statistical modeling. This presents a disadvantage to cognitive radar/sonar since optimizing waveform design becomes highly nonconvex. Modeling the clutter and target responses via random transfer functions known as channel matrices simplifies this waveform optimization problem. The goal of this paper is to explore the optimal receive architectures for target detection that emerge when these channel matrices are modeled as deterministic, and then as random using a Ricean channel model. A likelihood ratio test (LRT) is derived yielding the well-known coherent matched filter, and an average LRT (ALRT) test is derived using Bayesian integration. The detection performance of these receivers is assessed and compared via standard analyses yielding receiver operating characteristic (ROC) curves. It is shown that the optimal ALRT is not strictly a linear function of the data.
AB - Conventional techniques for characterizing clutter depend on covariance-based statistical modeling. This presents a disadvantage to cognitive radar/sonar since optimizing waveform design becomes highly nonconvex. Modeling the clutter and target responses via random transfer functions known as channel matrices simplifies this waveform optimization problem. The goal of this paper is to explore the optimal receive architectures for target detection that emerge when these channel matrices are modeled as deterministic, and then as random using a Ricean channel model. A likelihood ratio test (LRT) is derived yielding the well-known coherent matched filter, and an average LRT (ALRT) test is derived using Bayesian integration. The detection performance of these receivers is assessed and compared via standard analyses yielding receiver operating characteristic (ROC) curves. It is shown that the optimal ALRT is not strictly a linear function of the data.
KW - Average Likelihood Ratio Test (ALRT)
KW - Likelihood Ratio Test (LRT)
KW - Receiver Operating Characteristics (ROC)
KW - radar detection
UR - http://www.scopus.com/inward/record.url?scp=85112451014&partnerID=8YFLogxK
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U2 - 10.1109/RadarConf2147009.2021.9455266
DO - 10.1109/RadarConf2147009.2021.9455266
M3 - Conference contribution
AN - SCOPUS:85112451014
T3 - IEEE National Radar Conference - Proceedings
BT - 2021 IEEE Radar Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE Radar Conference, RadarConf 2021
Y2 - 8 May 2021 through 14 May 2021
ER -