Direction-of-Arrival Estimation for CS-MIMO Radar Using Subspace Sparse Bayesian Learning
We address the problem of direction-of-arrival (DOA) estimation for compressive sensing based multiple-input multiple-output (CS-MIMO) radar. The spatial sparsity of the targets enables CS to be desirable for DOA estimation. By discretizing the possible t
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Shanxi Electric Power Technical College, Taiyuan 030021, China [email protected] 2 Naval Command College, Nanjing 210016, China
Abstract. We address the problem of direction-of-arrival (DOA) estimation for compressive sensing based multiple-input multiple-output (CS-MIMO) radar. The spatial sparsity of the targets enables CS to be desirable for DOA estimation. By discretizing the possible target angles, a overcomplete dictionary is constructed for DOA estimation. A structural sparsity Bayesian learning framework is presented for support recovery. To improve the recovery accuracy and speed up the Bayesian iteration, a subspace sparse Bayesian learning algorithm is developed. The proposed scheme, which needs less iteration steps, can provides high precision DOA estimation performance for CS-MIMO radar, even at the condition of low signal-to-noise ratio and coherent sources. Simulation results verify the usefulness of our scheme. Keywords: Multiple-input multiple-output radar Angle estimation Subspace decomposition
Sparse Bayesian learning
1 Introduction Multiple-input multiple-output (MIMO) radar is a relatively new concept for radar system. By exploiting multiple antennas in both transmit and receive end, the extra visual antenna aperture is formed. The visual aperture makes the performance of MIMO radar better than the traditional phased array radar [1]. Generally speaking, the array geometry in MIMO radar can be divided into two categories, the uniform array and the nonuniform array. Elements in the uniform array geometry must be spaced at intervals no larger than half wavelength of the carrier signal thus to avoid phase ambiguity. Typical uniform arrays including linear arrays, uniform circular array and L-shape or rectangle array. The nonuniform array setup is much more flexible than the uniform array configuration [2]. The minimum redundancy linear array and the random array are belong to this kind of array. Direction-of-arrival (DOA) estimation is a fundamental problem in MIMO radar that has aroused extensive attention. Existing estimation algorithms including Capon [3], multiple signal classification (MUSIC) [4], the estimation method of signal parameters via rotational invariance techniques (ESPRIT) [5–7], the parallel factor analysis (PARAFAC) [8, 9]. However, the majority of the above algorithms are © IFIP International Federation for Information Processing 2016 Published by Springer International Publishing AG 2016. All Rights Reserved Z. Shi et al. (Eds.): IIP 2016, IFIP AICT 486, pp. 31–38, 2016. DOI: 10.1007/978-3-319-48390-0_4
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effectiveless with the nonuniform arrays. Algorithm Capon and MUSIC are effective with nonuniform configuration, they only perform well with large number of snapshot. Besides, additional prior information is needed in this algorithm, such as the number of targets, the noise level, et al. Recently, compressive sensing (CS) theory has attracted extensive attention in the field of array signal processing [10–12]. In this paper, we focus on the compressive se
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