Signal Model and Parameters Estimation of Statistical MIMO Radar Based on Distributed Compressed Sensing Signal Model and Parameters Estimation of Statistical MIMO Radar Based on Distributed Compressed Sensing

• Published in: Journal of radars = Lei da xue bao Vol. 1; no. 2; pp. 143 - 146
• Main Authors: Zhu, Ying, Zhang, Gong, Zhang, Jin-dong
• Format: Journal Article
• Language: English
• Published: China Science Publishing & Media Ltd. (CSPM) 02.08.2012
• Subjects: Distributed Compressed Sensing (DCS); Joint sparsity model; One-stage greedy algorithm; Orthonormal matching pursuit; Statistical Multiple-Input Multiple-Output (MIMO) radar
• ISSN: 2095-283X; 2095-283X
• DOI: 10.3724/SP.J.1300.2012.20016

Distributed Compressed Sensing (DCS) extends compressive sampling from single signal to multi-signal ensembles. It also enables joint recovery that exploits both intra- and inter-signal correlation structures. Statistical MIMO radar systems that are made up of widely separated transmit/receive antennas form distributed detection systems for targets among transmitters, targets and receivers. In this paper, DCS is applied to statistical MIMO radars, and through the analysis of sparisty of the delays of target echo signals in the range space, the idea is proposed to construct target scene by joining all received signals. It also establishes the joint sparsity model of received signals, and gives joint reconstruction algorithms that can estimate target parameters. Simulation results show that, compared with the algorithm based on CS, the one based on DCS increases the parameter estimation accuracy while offering a reduction in the number of measurements. It is also validated that DCS -MIMO radars can effectively overcome target RCS fluctuations.