TY - GEN
T1 - Estimation of structured covariance matrices for radar STAP under practical constraints
AU - Kang, Bosung
AU - Monga, Vishal
AU - Rangaswamy, Muralidhar
PY - 2014
Y1 - 2014
N2 - Estimation of the disturbance or interference covariance matrix plays a central role in radar target detection. Traditional maximum likelihood (ML) estimators lead to degraded false alarm and detection performance in the realistic regime of limited training. For this reason, structured covariance estimators have been actively researched. This paper reviews as well as proposes new structured covariance estimation methods which exploit physically motivated practical constraints. We first review the rank constrained maximum likelihood (RCML) estimator which explicitly incorporates the rank of the clutter subspace as a constraint in the ML problem. Next, we introduce an efficient approximation of structured covariance under joint Toeplitz and rank constraint (EASTR). In particular, we propose new quadratic optimization problems that enforce Toeplitz structure while preserving rank. Crucially, both the RCML estimator and the EASTR admit closed form solutions and hence facilitate real time implementation. We perform experimental evaluation in the form of normalized SINR, probability of detection, and whiteness tests. In each case, we compare against widely used existing estimators and show that exploiting the practical constraints has significant merits in covariance estimation.
AB - Estimation of the disturbance or interference covariance matrix plays a central role in radar target detection. Traditional maximum likelihood (ML) estimators lead to degraded false alarm and detection performance in the realistic regime of limited training. For this reason, structured covariance estimators have been actively researched. This paper reviews as well as proposes new structured covariance estimation methods which exploit physically motivated practical constraints. We first review the rank constrained maximum likelihood (RCML) estimator which explicitly incorporates the rank of the clutter subspace as a constraint in the ML problem. Next, we introduce an efficient approximation of structured covariance under joint Toeplitz and rank constraint (EASTR). In particular, we propose new quadratic optimization problems that enforce Toeplitz structure while preserving rank. Crucially, both the RCML estimator and the EASTR admit closed form solutions and hence facilitate real time implementation. We perform experimental evaluation in the form of normalized SINR, probability of detection, and whiteness tests. In each case, we compare against widely used existing estimators and show that exploiting the practical constraints has significant merits in covariance estimation.
UR - http://www.scopus.com/inward/record.url?scp=84906767823&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84906767823&partnerID=8YFLogxK
U2 - 10.1109/RADAR.2014.6875659
DO - 10.1109/RADAR.2014.6875659
M3 - Conference contribution
AN - SCOPUS:84906767823
SN - 9781479920341
T3 - IEEE National Radar Conference - Proceedings
SP - 585
EP - 590
BT - 2014 IEEE Radar Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE Radar Conference, RadarCon 2014
Y2 - 19 May 2014 through 23 May 2014
ER -