Research On Signal Processing Method Of Echo Of High-Speed Target Based On SFT

In recent years,with the development of the radar field and modern technology,the detection of high-speed targets and even ultra-high-speed targets has become one of the main tasks of the detection system,which also leads to more and more data that needs to be processed in real time.Obviously,the traditional discrete Fourier transform(DFT)has been unable to meet the requirements of fast processing.Currently,the most widely used fast Fourier transform(FFT),although greatly reducing the amount of calculation,however,the rapid development of the radar field requires more Fast signal processing technology.The sparse Fourier transform algorithm is a fast Fourier transform algorithm that takes advantage of the sparsity of the signal frequency domain.It can process only the large value points in the frequency domain for signals sparse in the frequency domain,by This greatly improves the processing speed of the frequency domain sparse signal and saves the time of signal calculation.For sparse signals in the frequency domain,the sparse Fourier transform algorithm can effectively obtain lower signal calculation complexity and the same Doppler spectrum estimation accuracy.In the high-frequency target radar,the Doppler spectrum of the high-speed target radar echo usually has sparse characteristics.Using the sparse Fourier transform algorithm to process the sparse signal in the frequency domain can effectively reduce the operation cost and improve the operation efficiency.In this paper,the high-speed target echo model is first established,the influencing factors of the high-speed target echo are studied,and the simulation shows the distance walking and Doppler spectrum diffusion phenomena of the highspeed target echo.Aiming at the phenomenon of distance walking of high-speed targets,the Keystone transform of distance walking compensation algorithm is introduced.Regarding the study of the sparse Fourier transform algorithm,this paper simulates and analyzes the aliasing sparse Fourier transform,the hardwareimplemented Big Band algorithm,and the sparse Fourier transform based on the frequency position polynomial.The above sparse Fourier transform algorithms all use the basic idea of downsampling.Time domain downsampling will result in aliasing of the frequency spectrum,so the basic idea of the sparse Fourier transform algorithm is downsampling and de-aliasing.This paper simulates the spectrum estimation results of different sparse Fourier transforms,and studies the performance of three sparse Fourier transforms,and gives the constraints of various sparse Fourier transforms.The simulation results show that under certain conditions,the sparse Fourier transform can achieve better computing efficiency than the fast Fourier transform.The above research results are based on uniform high-speed targets.For the target with acceleration,this paper simulates the spectral estimation results of three sparse Fourier transforms in the case of single target and multi-target.The results show that when the acceleration is small,All three kinds of sparse Fourier transform can obtain good spectral estimation results.Under the condition of large acceleration,FLP-SFT can still obtain good estimation results.Finally,this paper proposes a high-speed target signal detection algorithm based on sparse Fourier transform.First,the above-mentioned FLP-SFT with good performance is used.After the sparse Fourier transform,speed compensation is performed for the distance walking phenomenon in high-speed target detection.Then perform MTD moving target detection to estimate target speed and distance information.The above mentioned method can correctly and effectively perform target detection,and when the signal has sparse characteristics,the operation time lower than FFT can be obtained.Using the sparse Fourier transform to detect moving targets,the same result can be obtained.