For Review O nly MIMO Radar Imaging via SLIM

Through waveform diversity, multiple-input multiple-out put (MIMO) radar can provide higher resolution, improved sensitivity, and increased parameter identifiability compare d to more traditional phased-array radar schemes. Existing met hods for target estimation, however, often fail to provide accur ate MIMO angle-range-Doppler images when there are only a few data snapshots available. Sparse signal recovery algorithms, including manyl1-norm based approaches, can offer improved estimation in that case. In this paper, we present a regularized minimization approach to sparse signal recove ry. Sparse Learning via Iterative Minimization, or SLIM, follo ws an lq-norm constraint (for0 < q ≤ 1), and can thus be used to provide more accurate estimates compared to the l1-norm based approaches. We herein compare SLIM, through imaging examples and examination of computational complexity, to several well-known sparse methods, including the widely-u sed CoSaMP approach. We show that SLIM provides superior performance for sparse MIMO radar imaging applications at a low computational cost. Furthermore, we will show that the user parameter q can be automatically determined by incorporating the Bayesian information criterion. IEEE Transactions on Signal Processing Submitted in December, 2009 EDICS#: SAM-RADR 1This material is based on research sponsored in part by the U. S. Army Research Laboratory and the U. S. Army Research Office under c ont act/grant No. W911NF-07-1-0450, the National Science Foundation (NS F) under Grant No. ECCS-0729727, the Office of Naval Research under Gr ant No. N00014-09-1-0211, the Komen Breast Cancer Foundation unde r grant No. BCTR0707587, the SMART Fellowship Program, the Swedish Res earch Council (VR), and the European Research Council (ERC). The v iews and conclusions contained herein are those of the authors and sh ould not be interpreted as necessarily representing the official polic ies or endorsements, either expressed or implied, of the U.S. Government. The U.S . Government is authorized to reproduce and distribute reprints for Gove rnmental purposes notwithstanding any copyright notation thereon. 2Xing Tan is with the Department of Electrical and Computer En gineering, University of Florida, Gainesville, FL 32611-6130, USA. Ph one: (352) 3925241; Fax: (352) 392-0044; Email: tanxing@ufl.edu. 3William Roberts is with the Department of Electrical and Com puter Engineering, University of Florida, Gainesville, FL 32611 -6 30, USA. Phone: (352) 392-5241; Fax: (352) 392-0044; Email: wroberts83@ho tmail.com. 4Jian Li is with the Department of Electrical and Computer Eng ineering, University of Florida, Gainesville, FL 32611-6130, USA. Ph one: (352) 392-2642; Fax: (352) 392-0044; Email: li@dsp.ufl.edu. Please address all correspondence to Jian Li. 5Petre Stoica is with the Department of Information Technolo gy, Uppsala University, Uppsala, Sweden. Phone: 46-18-471-7619; Fax: 46-18-511925; Email: ps@it.uu. se. I. I NTRODUCTION A classical, phased-array radar consists of a set of antennas transmitting scaled versions of a single waveform. By adjusting the phase at each antenna, a phased-array radar can be used to concentrate a narrow beam of energy onto a scene of interest [1], [2]. In this way, more energy can be focused on a target, and a higher signal-to-noise ratio (SNR) can be achieved at the receiver stage of a system. Alternatively, a multiple-input multiple-output (MIMO) r adar, through waveform diversity and at an expense of reduced SNR, can be used to effectively image an entire scene of interest within a single coherent processing interval (see , e.g., [3]–[13]). Through careful construction of its transmissi on sequences, a MIMO radar can be used to achieve higher resolution [11], [12], improved sensitivity [11] (capable of detecting slower moving targets in the scene), and better parameter identifiability [14] (capable of uniquely estima ting the parameters of more targets). The increasing popularity of MIMO radar among researchers has led to further performance improvements through advanced waveform synthesis [15]– [21], beampattern design [22]–[25], and receive filter desi gn [15], [26]. The principal function of any radar is to provide an estimate for the location, speed, and amplitude of targets in a scene of interest [27]. At the receiver, data-independent estima tion techniques, including delay-and-sum (DAS) or matched filte ring, are traditionally adopted due to their low computation al burden and high-SNR properties. However, data-independen t approaches suffer from high sidelobe levels and low resolut ion. For a narrowband MIMO radar (since reflected waveforms will be linearly independent), adaptive beamforming approache s, such as CAPON [28] and APES [29], can instead be used to minimize interference and improve resolution. Data-depen dent approaches require a large number of data snapshots to provi de accurate detection, especially in the presence of high leve ls of noise or clutter interference. In practice, however, the nu mber of data snapshots is frequently restricted by the stationar ty of the scene. In most radar imaging applications, the number of targets in the scene of interest is substantially less than the numbe r of potential source locations. Sparse signal recovery techni ques, originally proposed in the statistics, signal processing a d machine learning communities (e.g., see [30]–[34]), can be used to provide more accurate target descriptions. For exam ple, the iterative adaptive approach (IAA), a nonparametri c and user-parameter free algorithm, was originally present ed in [35] to provide, together with the Bayesian information criterion (BIC), sparse signal representation for passive ensing, channel estimation, and single-antenna radar applica tions. In [26], IAA was extended to perform angle-range-Doppler imaging for MIMO radar, and was further shown to outperform conventional data-adaptive estimation techniques. IAA wa s proven to provide accurate estimation even when the number of data snapshots was low (or even when a single snapshot was obtained). Improved performance with IAA, however, comes at a cost of significantly increased computational burden at the receiver stage of the radar system (compared to more