Optimized Measurement Matrix Design Using Spatiotemporal Chaos for CS-MIMO Radar

Zhenni Peng,Jindong Zhang,Gong Zhang,De Ben

doi:10.1155/2014/916451

Abstract

We investigate the possibility of utilizing the chaotic dynamic system for the measurement matrix design in the CS-MIMO radar system. The CS-MIMO radar achieves better detection performance than conventional MIMO radar with fewer measurements. For exactly recovering from compressed measurements, we should carefully design the measurement matrix to make the sensing matrix satisfy the restricted isometry property (RIP). A Gaussian random measurement matrix (GRMM), typically used in CS problems, is not satisfied for on-line optimization and the low coherence with the basis matrix corresponding to the MIMO radar scenario can not be well guaranteed. An optimized measurement matrix design method applying the two-dimensional spatiotemporal chaos is proposed in this paper. It incorporates the optimization criterion which restricts the coherence of the sensing matrix and singular value decomposition (SVD) for the optimization process. By varying the initial state of the spatiotemporal chaos and optimizing each spatiotemporal chaotic measurement matrix (SCMM), we can finally obtain the optimized measurement matrix. Its simulation results show that the optimized SCMM can highly reduce the coherence of the sensing matrix and improve the DOA estimation accuracy for the CS-MIMO radar.

Highlights

The application of compressive sensing (CS) to radar systems has received considerable attention in recent years [1, 2]
The coherence of the sensing matrix will be calculated to show the effectiveness of using spatiotemporal chaos for measurement matrix design
The examples of DOA estimation will be given to demonstrate the excellent performance of the CS-multiple-input multiple-output (MIMO) radar with the optimized spatiotemporal chaotic measurement matrix (SCMM)

Summary

Introduction

The application of compressive sensing (CS) to radar systems has received considerable attention in recent years [1, 2]. The CS theory asserts that a signal that exhibits sparsity in some domain can be recovered from far fewer samples than that required by the Nyquist theory [3]. According to the CS theory, employing CS in multiple-input multiple-output (MIMO) radar can recover the target scene information from significantly fewer samples than the traditional methods. In colocated CS-MIMO radar, each of the receive antennas compresses its received signal via a transformation matrix, referred to as the measurement matrix. According to the RIP, an important property that measurement matrix Φ should obey is the low coherence with the basis matrix Ψ [4]. With an orthonormal basis matrix Ψ, the use of a random measurement matrix Φ leads to a sensing matrix Θ (Θ ≜ ΦΨ) that meets the RIP requirement with overwhelming probability. Since the basis matrix is constructed specially based on the given signal model in a MIMO radar scenario, the measurement matrix is expected to facilitate an efficient and controllable implementation so as to match the known basis matrix

Objectives

Results

Conclusion