Signal Processing Technology For Bistatic MIMO Radar

As a new radar system,Multiple-Input Multiple-Output(MIMO)has attracted wide attention once it was proposed.Bistatic MIMO radar combines the advantages of bistatic radar and MIMO radar.Because of its good performance in weak target detection,parameter estimation and anti-interference,it has become a hot topic of current research.This thesis focuses on the research of bistatic MIMO radar signal processing technology.The main research contents and results are as follows:The first part starts from the structure of the bistatic MIMO radar system and constructs the transmitting and receiving signals model of the bistatic MIMO radar.This chapter proposes a method of dividing the space according to the direction of the transmitting beam.Based on this method,this chapter proposes a signal processing method with a small amount of calculation.After that,it analyzes the calculation amount of this method and simulates the signal processing results.Finally,it formulates the signal obtained after matched filtering to obtain a matrix signal model and a tensor signal model for parameter estimation.The second part studies the bistatic MIMO radar angle estimation algorithm based on the matrix signal model.This part classifies the existing traditional algorithms and conducts computer simulation to evaluate the performance of the algorithms.In order to solve the problem of the high complexity of the Multiple Signal Classification(MUSIC)algorithm,this chapter proposes a local search algorithm.Under the condition of a uniformly equidistant linear array,the Estimating Signal Parameters via Rotational Invariance Techniques(ESPRIT)algorithm and the MUSIC algorithm are combined for angle estimation.While reducing the complexity of the algorithm,the algorithm also has high angle estimation accuracy.The third part studies the bistatic MIMO radar angle estimation algorithm based on the tensor signal model.This chapter focuses on the parallel factor(PARAFAC)algorithm and the highorder singular value decomposition(HOSVD)algorithm.Compared with the algorithm based on the matrix signal model,the tensor signal uses the multi-dimensional information of the received signal,which improves the angle estimation accuracy of the algorithm.The fourth part combines the real-valued processing method with the two models.This type of algorithm reduces the amount of algorithm calculations and improves algorithm performance by converting complex-valued operations into real-valued operations.This chapter proposes an algorithm that combines Compressed Sensing(CS)and PARAFAC based on real-value processing.This algorithm reduces the amount of calculation of PARAFAC decomposition and improves the accuracy of angle estimation.The simulation experiment results show that the algorithm has better performance when dealing with coherent targets and non-coherent targets.