Optimal Design And Parallel Implementation Of Frequency Division MIMO Signal Processing Based On GPU

Frequency division MIMO radar is gradually applied in various detection scenarios of radar because of its advantages of spatial signal frequency diversity,high parameter estimation performance and high energy utilization.MIMO radar needs to receive a mass of signal data,so the serial calculation approach cannot meet the real-time requirements of radar signal processing.The GPU architecture contains a large number of computing units,which are suitable for computing scenarios where the preceding and following computing steps are independent of each other,and provides a hardware basis for the parallel implementation of MIMO signal processing.In this thesis,we analyze the signal processing flow of frequency division MIMO radar based on centralized uniform area array,design an optimized signal processing flow,and build a frequency division MIMO radar signal processing system through the GPU-based ArrayFire parallel programming model.The main contents of this thesis are as follows:1.Based on the detection of low-altitude and slow-speed targets,a centralized uniform area array MIMO radar detection simulation scenario is designed.Then,a spatial coordinate system is established,and the frequency-division MIMO array transmited signal and echo signal models are theoretically analyzed and derived.The transmitting and receiving steering vector matrices of the uniform area array are designed.The transmit array is designed as the antenna arrangement 2×2,and the receive array is designed as the antenna arrangement 8×8.The frequency division chirp signal of four carrier frequencies is simulated.2.The principle of conventional signal processing flow of the frequency division MIMO radar is analyzed.According to the characteristics of the frequency domain signal,this thesis designs an optimized signal processing: First,the DBF is placed after the MTD processing because the processing order of the DBF does not affect the signal processing results.Frequency Domain Filtering,Pulse Compression,MTI,and MTD can be comprehensively processed in the frequency domain,which reduces the multiple time-frequency domain conversions in the conventional process.Second,for the window processing of MTD and DBF,it can be pre-computed and stored when the signal parameters are known,so it can be pre-computed and stored,so these windows can be performed before signal processing,which can reduce the amount of redundant computation.For the optimized signal processing flow,the theoretical calculation amount of each part is analyzed.Compared with the conventional flow,the theoretical operation of the optimized flow is reduced to a quarter.3.The four signal processing models of frequency domain integrated filtering,frequency domain MTD,space domain DBF,and time domain one-dimensional CFAR in the optimized signal processing flow are analyzed and designed for parallelism,and the parallel algorithm is implemented using the GPU-based ArrayFire programming model.Compared with the operation results of the two signal processing processes implemented by Matlab and the optimized processing process implemented by ArrayFire,the parallel algorithm of ArrayFire achieves a speedup ratio of 65 times.4.In order to improve the reliability of the optimized acceleration system,this thesis uses a hardware-in-the-loop simulation platform with two transmitters and four receivers to verify the frequency-division MIMO optimized signal processing process.After simulation experiments,the MIMO optimized signal processing model can correctly process the received measured signal data.The measured target distance and speed are within a theoretical range,which verifies the reliability of the optimized signal processing process.