Research On Ultra-wideband Impulse Radar Low Signal-to-noise Ratio Echo Anti-jamming Signal Processing Technology

As a new type of radar,Ultra-wideband impulse radar is a carrierless radar that recognizes targets by transmitting pulses with nanosecond width.It has the advantages of high resolution,low power loss and strong penetration ability,and has been widely used in various fields,including the monitoring of buildings and mechanical equipment,the detection of underground targets,the monitoring of ultra-low altitude targets,the monitoring of stealth and occlusion targets,high-resolution images and target identification.However,the energy of the signal emitted by ultra-wideband impulse radar is mainly concentrated in high frequency,which is easily affected by various electrical signals and communication signals,and it is difficult to accurately identify and extract target signals in a complex electromagnetic field environment.Therefore,the research focus of this thesis is on Ultra-wideband impulse radar echo signal processing technology.In order to improve the filtering performance of the existing variable-step LMS algorithm in the environment of low signal-to-noise ratio,a variable-step LMS algorithm based on sinh function and logarithmic function is proposed.According to the principle of variable step LMS adaptive adjustment,the nonlinear function between the new step factor and the error is constructed,and the influence of the values of key parameters in the new algorithm on the filtering performance is analyzed,and the performance comparison is compared with the s-function,logarithmic function improvement algorithm and hyperbolic sine function algorithm.Finally,the new algorithm is applied to Ultra-wideband impulse radar echo processing.The simulation results show that the algorithm improves the convergence speed while ensuring a small steady-state error value,which is better than the above algorithm.In echo signal processing,when 5 d B,10 d B,and 15 d B noise are added respectively,the algorithm greatly improves the convergence speed,steady-state error and anti-interference performance.In order to overcome the shortcomings of traditional hard and soft threshold functions and improve the accuracy of signal reconstruction,a wavelet denoising algorithm by improving the threshold function is proposed by studying the denoising principle of the threshold function and the rules for optimizing the threshold function.The new threshold function is analyzed from the aspects of continuity,asymptotic and bias,and its feasibility is proved theoretically.The new threshold function algorithm is applied to the Ultra-wideband impulse radar echo processing to compare and analyze the signal-to-noise ratio and mean squared error filtered by the traditional soft and hard threshold function and the new threshold function.Simulation results verify: Compared with the traditional hard denoising and soft threshold function denoising methods,the signal-to-noise ratio of the new threshold function is higher than that of the traditional soft and hard threshold function,and the mean squared error is lower than that of the traditional soft and hard threshold function.In order to improve the problem of low flexibility of traditional threshold,the LMS algorithm is applied to the new threshold function algorithm of wavelets,and an adaptive threshold adjustment wavelet denoising algorithm is proposed.By introducing the particle swarm optimization algorithm,under the influence of different signal-tonoise ratios,the self-adaptor can automatically adjust the output threshold to gradually approach the optimal threshold parameter,so as to better realize wavelet denoising.The simulation verifies that the adaptive threshold wavelet denoising algorithm is superior to the new threshold function wavelet algorithm,and the signal-to-noise ratio and mean squared error maintain good performance.