Design And Implementation Of Tracking And Measurement Radar Signal Processor Based On GPU

The requirement enhances continually in parameter measurement precision and real-time capability for modern tracking and measurement radar,making the processing data size and the processing speed of signal processor need to improve accordingly.Compared with the traditional signal processing solution with FPGA and DSP,solution based on GPU owns lower cost,better programmability,stronger computing power.In order to satisfy the real-time requirement of radar signal processing,this topic studys the design and realization of tracking and measurement radar signal processor based on GPU and Linux operating system.Firstly,this paper studys the working principle of monopulse tracking radar,and researches development status that uses GPU to accelerate signal processing in the radar field at home and abroad,and discourses research background and research significance of tracking and measurement radar signal processing based on GPU.Then,the paper introduces several algorithms and principles involved in the process of target acquisition and tracking in detail.And then,on the basis of the algorithm theory,the paper describes the design process of tracking measurement radar signal processor detailedly,and introduces the components of signal processor.The paper illustrates the design details of the software module.The software module contains display control module,echo data reception module,echo data processing module,and processing result output module.Finally,the paper illustrates the implementation process of signal processing software.On the basis of a brief introduction for CUDA programming model,the paper introduces software implementation details of GPU acceleration card one,GPU acceleration card two and CPU.The paper compares Matlab simulation time with GPU implementation time,found the latter is faster than the former in 126 times for GPU card one and 125 times for GPU card two.On this basis,the paper introduces some optimization strategy of the GPU programme,and optimizes the GPU program progressively that has been implemented.Compared with the GPU program before optimization,the optimized GPU program improves 51.2%for GPU card one and 66.2% for GPU card two in operation efficiency.