Research On Several Key Technologies Of MIMO Radar

Multiple Input Multiple Output (MIMO) radar as a new type radar system, compared with the conventional phased array radar and multistatic radars, has many advantages in target detection, parameter estimation, target localization and tracking et al. This dissertation mainly studies the design of MIMO radar orthogonal waveform, receiving processing and detection, target localization and tracking, involving the chaos theory, frequency hopping. Ultra Wide Band (UWB), Space-Time Block Coding (STBC), wavelet transform, compressive sensing (CS), particle filter (PF) and so on. The main works and contributions are as followings:(1) In order to achieve the orthogonality of the MIMO waveform, the Orthogonal Frequency Division Multiplexing-Linear Frequency Modulation(OFDM-LFM) singals are researched, and then the orthogonal waveforms based on the chaotic theory are designed. The chaotic frequency hopping signals are presented. Based on this theory, combined with the STBC and the chaotic binary-phase codes modulated by UWB signals, STBC MIMO radar waveforms are proposed. The ability of recognition of complex target is analyzed and the detection probability based on the Neyman-Pearson criterion is derived. The simulation results show the superior performance of this UWB signal.(2) The signal receiving and processing technologies of MIMO radar are studied. First, apply the method of wavelet analysis to the denoising of MIMO radar. In accordance with the LFM and chaotic frequency hopping signals’ processing, the wavelet analysis denoising is used, which has a good effection and improves the dectection performance. Then particle filter is proposed to separate the orthogonal waveforms. Before the processing of matched filter (MF), the chaotic signals separation is used, which can achieve the denoising and decorrelaiton function and has a better output to improve the ability of target detection and resolution.(3) To reduce the complexity of MIMO signal processing, the CS theory is applied to the MIMO radar and its sparse array. The designed chaotic signals are sparse decomposition and recovery with the Matching Pursuit (MP) and Orthogonal Matching Pursuit (OMP) arithmetics. The sparse array beam forming of MIMO radar is studied and the simulation comparison between stochastic embattling and Uniform Linear Array (ULA) is given, the resolution performance differences between the conherent MIMO radar and non-coherent MIMO radar are simulated and researched. After analysis the correlation coefficient of the stochastic embattling and ULA, conherent and non-conherent systems, the simulation results show the advantage of stochastic embattling and lower correlation coefficient of the compressive matrix means higher resolution; Combined with the OMP arithmetic, the Direction of Arrival (DOA) estimation of monostatic MIMO and Direction of Arrival and Direction of Departure (DOA-DOD) joint estimation of bistatic MIMO radar are proposed and simulated, which can reduce the amount of data processing.(4) Base on the localization of MIMO radar, the Cramer-Rao lower bound (CRLB) matrix of target location is derived and the model of time-varying targets is built, the Rao-Blackwellized Particle Filter (RBPF) for multi-targets tracking in MIMO radar is proposed.This method can effectively and dynamically track time-varying number targets and achieve higher tracking accuracy. In the meanwhile, according to the requirement for ground target tracking, the Multiple-Input Single-Output (MISO) radar system is proposed. An improved Interacting Multiple Model Particle Filter (IMM-PF) algorithm is designed and deeply studied for this system.(5) Based on the phase-array radar technology, the phase array MIMO radar system structure is designed, which is a good compromise proposal. Make use of the time division multiplexing (TDM) method, the Single-Input Single-Output (SISO) radar system can realize the function of the MIMO radar system, which can reduce the system cost. In the end, the develop trend and application of MIMO are discussed.