Signal processing algorithms for mimo radar

Abstract

The main contribution of this thesis is to study the signal processing issues in MIMO radar and propose novel algorithms for improving the MIMO radar system. In the first part of this thesis, we focus on the MIMO radar receiver algorithms. We first study the robustness of the beamformer used in MIMO radar receiver. It is known that the adaptive beamformer is very sensitive to the DOA (direction-of-arrival) mismatch. In MIMO radar, the aperture of the virtual array can be much larger than the physical receiving array in the SIMO radar. This makes the performance of the beamformer more sensitive to the DOA errors in the MIMO radar case. In this thesis, we propose an adaptive beamformer that is robust against the DOA mismatch. This method imposes constraints such that the magnitude responses of two angles exceed unity. Then a diagonal loading method is used to force the magnitude responses at the arrival angles between these two angles to exceed unity. Therefore the proposed method can always force the gains at a desired interval of angles to exceed a constant level while suppressing the interferences and noise. A closed form solution to the proposed minimization problem is introduced, and the diagonal loading factor can be computed systematically by a proposed algorithm. Numerical examples show that this method has an excellent SINR (signal to noise-plus-interference ratio) performance and a complexity comparable to the standard adaptive beamformer. We also study the space-time adaptive processing (STAP) for MIMO radar systems. With a slight modification, STAP methods developed originally for the single-input multiple-output (SIMO) radar (phased array radar) can also be used in MIMO radar. However, in the MIMO radar, the rank of the jammer-and-clutter subspace becomes very large, especially the jammer subspace. It affects both the complexity and the convergence of the STAP algorithm. In this thesis, we explore the clutter space and its rank in the MIMO radar. By using the geometry of the problem rather than data, the clutter subspace can be represented using prolate spheroidal wave functions (PSWF). Using this representation, a new STAP algorithm is developed. It computes the clutter space using the PSWF and utilizes the block diagonal property of the jammer covariance matrix. Because of fully utilizing the geometry and the structure of the covariance matrix, the method has very good SINR performance and low computational complexity. The second half of the thesis focuses on the transmitted waveform design for MIMO radar systems. We first study the ambiguity function of the MIMO radar and the corresponding waveform design methods. In traditional (SIMO) radars, the ambiguity function of the transmitted pulse characterizes the compromise between range and Doppler resolutions. It is a major tool for studying and analyzing radar signals. The idea of ambiguity function has recently been extended to the case of MIMO radar. In this thesis, we derive several mathematical properties of the MIMO radar ambiguity function. These properties provide some insights into the MIMO radar waveform design. We also propose a new algorithm for designing the orthogonal frequency-hopping waveforms. This algorithm reduces the sidelobes in the corresponding MIMO radar ambiguity function and makes the energy of the ambiguity function spread evenly in the range and angular dimensions. Therefore the resolution of the MIMO radar system can be improved. In addition to designing the waveform for increasing the system resolution, we also consider the joint optimization of waveforms and receiving filters in the MIMO radar for the case of extended target in clutter. An extended target can be viewed as a collection of infinite number of point targets. The reflected waveform from a point target is just a delayed and scaled version of the transmitted waveform. However, the reflected waveform from an extended target is a convolved version of the transmitted waveform with a target spreading function. A novel iterative algorithm is proposed to optimize the waveforms and receiving filters such that the detection performance can be maximized. The corresponding iterative algorithms are also developed for the case where only the statistics or the uncertainty set of the target impulse response is available. These algorithms guarantee that the SINR performance improves in each iteration step. The numerical results show that the proposed iterative algorithms converge faster and also have significant better SINR performances than previously reported algorithms. (Abstract shortened by UMI.)