Research On Key Technology Of Signal Processing In LPI Frequency-Coded Radar Receiver

As a signal form with large time-bandwidth product and low probability of intercept,frequency-coded signals have important military application value in modern warfare environment.Therefore,it is of great significance to study the signal processing methods for frequency-coded radar signals.The work of this dissertation is focused on the synthesis of high-resolution range profiles and motion compensation for frequency stepped and Costas-coded radar signals.The dissertation is divided into the following three parts:In the first part,first,the definition of frequency-coded signal is introduced,and the ambiguity function expression for frequency-coded signal is given out.Then,by means of the ambiguity function,the range and Doppler resolution performance of the frequency stepped and Costas-coded radar signals are analyzed.In the second part,firstly,aiming at range-Doppler coupling of frequency stepped signals,the target echo model of the signal is established.Secondly,the effect of target motion on one-dimensional range profile of the coherent synthesis is analyzed in detail.Then,a velocity estimation method combining the cross-correlation function Fast Fourier Transformation and the minimum burst error method is proposed.The proposed method greatly reduces the computational complexity while ensuring the estimation accuracy.Meanwhile,the method avoids estimation error caused by local extremum effectively.The method has the advantages of high velocity measurement accuracy and good anti-noise performance.In the third part,the effect of target motion on the synthetic range profile produced by Costas-coded radar signals is studied.The dissertation introduces the waveform entropy as the objective function to measure the influence of the target motion on the range profile.Then,two motion compensation methods based on particle swarm and hybrid particle swarm are proposed.The two methods are compared with the traditional simulated annealing algorithm,respectively.Simulation experiments show that the two proposed methods accurately estimate the target velocity even under low signal-to-noise ratio.Besides,the two proposed methods have a low computational complexity and retrieve accurate velocity estimates,suggesting their engineering application value.Furthermore,comparing the two proposed methods,the particle swarm optimization method is suitable for high precision conditions and the hybrid particle swarm is suitable for environments with small computational complexity.