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.
Conference Paper: A new algorithm for DOA estimation in colocated MIMO array[Show abstract] [Hide abstract]
ABSTRACT: The problem of direction of arrival (DOA) for multiple-input multiple-output (MIMO) array systems is considered, where orthogonal waveforms are transmitted simultaneously. A novel DOA estimation algorithm for collocated MIMO array is proposed. The proposed algorithm exploits the time delay to obtain two cross-correlation matrices, where the effect of additive noise is eliminated. Then we use singular value decomposition (SVD) technique to achieve the signal subspace matrix. Finally, an ESPRIT-like method is used to resolve the signal subspace and achieve the estimates of DOA. Simulation results confirm the effectiveness of the proposed algorithm.Signal Processing, Communications and Computing (ICSPCC), 2011 IEEE International Conference on; 01/2011
Conference Paper: Sparse Sampled MIMO radar for angle-range-doppler imaging[Show abstract] [Hide abstract]
ABSTRACT: MIMO radar can provide higher resolution, improve sensitivity, and increase parameter identifiability without considering sparse sampled. Sparse signal recovery algorithms can offer improved estimation when the scene of interest contains a limited number of targets. In this paper, we present a modified approach to sparse signal recovery. The proposed approach follows an lq-norm constraint (for 0Computational Problem-Solving (ICCP), 2012 International Conference on; 01/2012
Conference Paper: Robust DOA Estimation in MIMO Radar with Transmitting Uncertainties[Show abstract] [Hide abstract]
ABSTRACT: This paper is addressed to the problem of direction-of-arrival (DOA) estimation in multi-input multi-output (MIMO) radar. The single carrier and the multi carrier MIMO radars are considered. We introduce a special MIMO radar model with uncertainties due to uncalibrated transmitting array and diversity of radar cross section on different carriers. We use two DOA estimation techniques with robustness to model uncertain-ties. The first method is the subspace rank reduction (RARE) algorithm and the second method is the robust Capon algo-rithm. In multicarrier case, we propose waveform interpolation preprocessing which eliminates the signal decorrelation on the different subcarriers caused by Doppler effect. Effectiveness of the proposed techniques are confirmed by simulation results.Sensor Array and Multichannel Signal Processing Workshop (SAM), 2014 IEEE 8th, 505 - 508; 06/2014