Measurement Matrix Design for Compressive Sensing-Based MIMO Radar

• Published in: IEEE transactions on signal processing Vol. 59; no. 11; pp. 5338 - 5352
• Main Authors: Yao Yu, Petropulu, A. P., Poor, H. V.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.11.2011; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied sciences; Coherence; Compressive sensing; Criteria; Detection; Detection, estimation, filtering, equalization, prediction; direction of arrival (DOA) estimation; Exact sciences and technology; Gaussian; Information, signal and communications theory; Matrices; measurement matrix; MIMO radar; multiple-input multiple-output (MIMO) radar; Optimization; Position (location); Radar; Receiving antennas; Sampling, quantization; Sensors; Signal and communications theory; Signal, noise; Sparse matrices; Studies; Symmetric matrices; Telecommunications and information theory; Transmitting antennas; Waveforms; Performance evaluation; direction of arrival (DOA) estimation; Parameter estimation; Direction-of-arrival estimation; Random matrix; Signal estimation; measurement matrix; Optimization; Optimal design; Accuracy; Compressive sensing; Linear combination; Radar; Localization; MIMO system; multiple-input multiple-output (MIMO) radar; High performance; Stepped impedance device; Signal to interference ratio; Radar target; Simulation; Linear transformation; Coherence; Signal processing; Optimality criterion; Compressed sensing
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2011.2162328

In colocated multiple-input multiple-output (MIMO) radar using compressive sensing (CS), a receive node compresses its received signal via a linear transformation, referred to as a measurement matrix. The samples are subsequently forwarded to a fusion center, where an l 1 -optimization problem is formulated and solved for target information. CS-based MIMO radar exploits target sparsity in the angle-Doppler-range space and thus achieves the high localization performance of traditional MIMO radar but with significantly fewer measurements. The measurement matrix affects the recovery performance. A random Gaussian measurement matrix, typically used in CS problems, does not necessarily result in the best possible detection performance for the basis matrix corresponding to the MIMO radar scenario. This paper considers optimal measurement matrix design with the optimality criterion depending on the coherence of the sensing matrix (CSM) and/or signal-to-interference ratio (SIR). Two approaches are proposed: the first one minimizes a linear combination of CSM and the inverse SIR, and the second one imposes a structure on the measurement matrix and determines the parameters involved so that the SIR is enhanced. Depending on the transmit waveforms, the second approach can significantly improve the SIR, while maintaining a CSM comparable to that of the Gaussian random measurement matrix (GRMM). Simulations indicate that the proposed measurement matrices can improve detection accuracy as compared to a GRMM.