Research Of Signal Reconstruction Algorithm Based On Compressed Sensing

With the development of modern radar system,most of the signals faced by radar reconnaissance are characterized by large bandwidth,diverse modulation modes and complex waveforms,and the modern electromagnetic environment is becoming more and more complex,which makes the subsequent signal processing more difficult,so the radar recognition under various harsh conditions becomes more important.In view of the problem that the sampling frequency required by the traditional Nyquist theorem is not less than twice the bandwidth,it is difficult to process large bandwidth signals.Compressed sensing theory points out that for sparse signals and compressible signals,the signals can be sampled at a much lower rate than the traditional Nyquist sampling rate,and then the reconstruction algorithm is used to recover the original signal with a small amount of observation data without distortion Therefore,compressed sensing has become a research hotspot in the field of signal processing.This paper systematically carried out the application research of compressive sensing in radar signal recognition,using compressed sensing to reduce the front-end sampling pressure,reduce the data storage requirements of the sampling equipment,in-depth study of the existing greedy iterative class reconstruction algorithm,focusing on the reconstruction accuracy,computational complexity,efficiency and other performance of the algorithm,analyzes the advantages and disadvantages of the existing algorithm And then carry out specific research and improvement.The main contents of this paper are as follows:1.This paper introduces the three core contents of compressed sensing and several compression sampling frameworks of compressed sensing,meanwhile discusses the practical application scenarios of each compression sampling framework.It focuses on how the modulation wideband converter samples the analog signal,how to process the sampled data and the influence of parameters on signal reconstruction.2.An improved compressed sampling matching pursuit algorithm is proposed to overcome the shortcomings of the compressed sampling matching pursuit algorithm.The accuracy and efficiency of the Co Sa MP algorithm are low when SNR is low.In order to reduce the coherence between atoms in the sensing matrix,the pseudo inverse of the sensing matrix is processed before the iteration.The iteration process is divided into two steps.Firstly,the orthogonal matching pursuit(OMP)algorithm is used to pre select the support set,and then the support set and the updated residual after K iterations are taken as the input of the Co Sa MP algorithm,which solves the problems of low reconstruction accuracy and high algorithm complexity under low SNR.Simulation results show that the performance and advantages of the proposed algorithm are better than the existing greedy iterative algorithms in the case of low signal-to-noise ratio.3.An improved sparsity adaptive matching pursuit(MSAMP)algorithm is proposed for the case of unknown sparsity.The improved principle of the algorithm is analyzed in detail,and the selection of parameters is simulated.Under the optimal parameter value,the influence of the algorithm on the reconstruction accuracy and algorithm complexity of radar signal is analyzed.The simulation results show that the proposed algorithm can estimate the sparse value well under the premise of unknown sparsity.When the signal-to-noise ratio is high,the reconstruction accuracy is high and the algorithm complexity is low.