Advances and New Opportunities in MIMO Radar - Theoretical Analysis and Algorithms

Abstract

The thesis focuses on researching the multiple-input multiple-output (MIMO) radar. Particular topics are the study of ambiguity properties by exploiting transmit beamspace (TB) strategies, opportunities and challenges of clutter and jammer suppression in both the conventional and TB-based MIMO radar configurations, waveform designs ensuring good correlation properties, and joint multi-dimensional transmission and receive adaptive design guaranteeing best signal-to-interference-plus-noise ratio performance. A detailed overview of the MIMO radar research related to the above research aspects is presented, including subjects such as waveform(s)/code(s) design, clutter and jammer suppression, TB design, and parameter estimation and detection. The TB-based MIMO radar AF which deals with the case of far-field targets and narrowband waveforms is proposed. It incorporates the effects of transmit coherent processing gain, waveform diversity, and the array geometry in its definition, and can serve as a generalized AF form for which the phased-array (PA) and conventional MIMO radar AFs are important special cases. It shows interesting relationships with the existing AF results. Furthermore, the maximum achievable ''clear region'' of the TB-based MIMO radar AF, which is free of sidelobes, is derived, and two limiting cases that help obtain tight bounds for the ''clear region'' are identified. For addressing the clutter and jammer suppression problem, a series of reduced-dimensional (RD) spatial and/or temporal adaptive processing algorithms with reasonable complexity are developed, including the space-(fast) time adaptive processing algorithms which can maintain the cold clutter stationarity over the slow time domain, 3D space-time adaptive processing (STAP) algorithms for joint hot and cold clutter mitigation, and RD beamspace and robust beamforming techniques. Fast and efficient algorithms for generating aperiodic unimodular waveforms with good correlation properties are also proposed. The waveform designs are based on minimizing the integrated sidelobe level (ISL) or weighted ISL (WISL) of waveforms and are formulated as nonconvex quartic optimization problems in frequency domain. By means of the majorization-minimization technique, the quartic problems are then simplified into quadratic ones, where the inherent algebraic structures in the objective functions are exploited. For the WISL minimization based design, an alternative quartic form that allows to apply the quartic-quadratic transformation is additionally derived. The developed algorithms are applicable to large-scale design problems as they have faster convergence speed and lower complexity than the state-of-the-art algorithms. In addition, an efficient approach for jointly synthesizing the space-(slow) time transmission with unimodular waveforms and designing the receive STAP filter is proposed by means of the minorization-maximization technique. Two cases of known Doppler information and Doppler uncertainty on clutter bins are considered. The proposed algorithms demonstrate good performance with fast convergence speed and low complexity.