Target detection and parameter estimation for MIMO radar systems
ABSTRACT We investigate several target detection and parameter estimation techniques for a multiple-input multiple-output (MIMO) radar system. By transmitting independent waveforms via different antennas, the echoes due to targets at different locations are linearly independent of each other, which allows the direct application of many data-dependent beamforming techniques to achieve high resolution and excellent interference rejection capability. In the absence of array steering vector errors, we discuss the application of several existing data-dependent beamforming algorithms including Capon, APES (amplitude and phase estimation) and CAPES (combined Capon and APES), and then propose an alternative estimation procedure, referred to as the combined Capon and approximate maximum likelihood (CAML) method. Via several numerical examples, we show that the proposed CAML method can provide excellent estimation accuracy of both target locations and target amplitudes. In the presence of array steering vector errors, we apply the robust Capon beamformer (RCB) and doubly constrained robust Capon beamformer (DCRCB) approaches to the MIMO radar system to achieve accurate parameter estimation and superior interference and jamming suppression performance.
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ABSTRACT: Sparse linear arrays provide better performance than the filled linear arrays in terms of angle estimation and resolution with reduced size and low cost. However, they are subject to manifold ambiguity. In this paper, both the transmit array and receive array are sparse linear arrays in the bistatic MIMO radar. Firstly, we present an ESPRIT-MUSIC method in which ESPRIT algorithm is used to obtain ambiguous angle estimates. The disambiguation algorithm uses MUSIC-based procedure to identify the true direction cosine estimate from a set of ambiguous candidate estimates. The paired transmit angle and receive angle can be estimated and the manifold ambiguity can be solved. However, the proposed algorithm has high computational complexity due to the requirement of two-dimension search. Further, the Reduced-Dimension ESPRIT-MUSIC (RD-ESPRIT-MUSIC) is proposed to reduce the complexity of the algorithm. And the RD-ESPRIT-MUSIC only demands one-dimension search. Simulation results demonstrate the effectiveness of the method.The Scientific World Journal 01/2013; 2013:784267. · 1.73 Impact Factor
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ABSTRACT: This paper is concerned with a multiple-input-multiple-output (MIMO) radar operating in an environment with two or more closely located targets. In this scenario, mutual target interference is a serious problem for multitarget parameter estimation, reducing the performance of existing methods such as least squares, Capon, and amplitude and phase estimation. In contrast to previous methods where the overall effect of mutual target interference is treated as “noise,” in this paper, two methods for suppressing this interference are proposed. The first is based on a constrained optimization problem that provides an iterative method. The second involves a novel nonlinear optimization based on a cost function of targets' directions which is solved using the biogeography-based-optimization algorithm. The performances of both the proposed approaches are evaluated via computer simulation studies and shown to outperform existing methods.IEEE Transactions on Geoscience and Remote Sensing 06/2013; 51(6):3683-3693. · 3.47 Impact Factor
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ABSTRACT: The direction estimation problem of coherent targets in multiple-input multiple-output (MIMO) radar systems is studied and a scheme with joint transmission and reception diversity smoothing is proposed. When both the transmitting and receiving antenna arrays are located closely in space, the new approach leads to much more available covariance matrices for spatial smoothing to decorrelate the coherent signals. As a result, a better estimation performance is achieved compared to the existing transmission diversity smoothing (TDS) method. It can also identify more coherent targets when sparse antenna arrays are employed. On the other hand, the proposed approach can be applied to joint direction of arrival (DOA) and direction of departure (DOD) estimation using existing direction estimation algorithms when the transmit and receive arrays are separated far away from each other (i.e. the bistatic case). Two specific methods are proposed under the scheme, one is based on forward-only (FO) spatial smoothing and one is based on forward-backward (FB) processing. Due to the increased number of covariance matrices for spatial smoothing, a further improved performance is achieved by the FB-based one.IEEE Journal of Selected Topics in Signal Processing 01/2014; 8(1):115-124. · 3.30 Impact Factor