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: Adaptive parameter estimation in MIMO radar[Show abstract] [Hide abstract]
ABSTRACT: In multiple-input multiple-output radar, independent waveforms are transmitted from different antennas, and the target parameters are estimated via the linearly independent echoes from different targets. Several adaptive approaches are directly applied to target angle and target amplitude estimation, including Capon, APES (amplitude and phase estimation). The CCA (canonical correlation analysis) approach is first proposed to estimate target locations which has high peak amplitudes, then a gradient-based algorithm is presented to improve the target angle estimation accuracy based on Capon approach which has a high resolution. With an initial angle, the angle sequence is iteratively updated with adaptive steps and converges to local peaks which indicate the target locations. Simulations show that the target angle accuracy is improved, and the common DOA (direction-of-arrive) problem is avoided.2013 International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC); 12/2013
- 03/2015; 12(4).
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ABSTRACT: An algorithm based on sparse representation for joint angle and Doppler frequency estimation in multiple-input multiple-output radar is proposed. Through the data reconstruction, the algorithm only requires the dictionary for one-dimensional angle [e.g. direction of departure (DOD)], which reduces the computational complexity compared to conventional method using dictionary for two-dimensional angle. The DOD can be estimated by finding the non-zero rows in the recovered matrix, which also contains the information of the direction of arrival (DOA) and the Doppler frequency, and they can be achieved via singular value decomposition and least squares (LS) principle. The estimated DOD, DOA and Doppler frequency can be automatically paired and the parameter estimation performance of the proposed algorithm is better than that of estimation of signal parameters via rotational invariance techniques (ESPRIT)-based algorithm and parallel factor (PARAFAC) method. Furthermore, the proposed algorithm requires no knowledge of the number of targets and works well for coherent targets. Simulation results verify the effectiveness of the algorithm.Multidimensional Systems and Signal Processing 01/2013; · 1.58 Impact Factor