Research On UWB Radar Signal Detection Based On Compressed Sensing

Electronic reconnaissance,as an important technique for obtaining enemy intelligence,is a key technology in the field of electronic countermeasures.As one of the commonly used time-varying signals in the radar field,linear frequency modulation(LFM)signal detection and related parameter estimation are important research contents in the field of electronic reconnaissance.Ultra-wideband LFM signal is gradually applied in combat because of its many advantages,but due to its high sampling rate,there are certain difficulties in ultrawideband signal detection and parameter estimation.The emergence of Compressed Sensing(CS)theory puts forward a new solution to this problem.The main contents of compressed sensing theory is studied in this thesis,which including sparse representation,compression sampling and reconstruction algorithms.Starting from the random equivalent sampling and AIC measurement framework,the commonly used analog sampling technology is analyzed and simulated.The experimental results show that the discrete data obtained by the above two methods can be used for signal reconstruction,and the reconstruction error is small.The signal detection problem under the compressed sensing framework is studied in this thesis.And the signal detection algorithm based on orthogonal matching tracking and the signal detection algorithm based on the maximum sparse coefficient location are mainly studied.In order to solve the problem that the above algorithms have low detection performance under low signal-to-noise ratio,the compressive sensing signal detection algorithm based on the characteristics of observation data is studied.The simulation results show that the algorithm can obtain higher detection probability under low SNR conditions.In this thesis,with the help of compressed sensing theory,the problem of detection and parameter estimation of ultra-wideband LFM signals with band-limited Gaussian noise is studied.The main works are done as follows: the parameter estimation algorithm of ultrawideband LFM signal based on redundant dictionary is studied.The estimation performance of the algorithm is evaluated from signal to noise ratio and the number of compressed sampling points.When the parameter estimation accuracy is relatively high,the computational complexity of the above algorithm is too high.An ultra-wideband LFM signal parameter estimation algorithm based on the Fr FT dictionary is studied.By using a two-step search strategy,the estimation accuracy of the algorithm is improved.and the amount of computation is reduced.