Pre-processing Of A Sparse Ultrawideband Receiver For Electronic Intelligence

With the development of science and technology,the frequency range which need to be scouted becomes very broad in electronic intelligence reconnaissance.The traditional Nyquist sampling theory encounters lot of difficulties in this situation.The introduction of CS theory provides a new way to solve these problem.Preprocessing of detection signal is a key step in electronic reconnaissance system.Its main task is to get parameters in time and frequency domain,and get pulse description word(PDW).Based on the CS theory,this paper studies the method of reconnaissance signal preprocessing.The main contents include the following:1.The basic principle of compressed sensing and its application in electronic reconnaissance are studied,including the sparse representation of signal,the design of the compression observation matrix and the method of reconstructing the original signal,etc.2.The most prominent architectures of CS-based A2 I converters are studied,including random demodulator(RD),modulated wideband converter(MWC),random modulation preintegrator(RMPI),and Non-uniform sampler(NUS).Meanwhile,simulation of NUS is carried out in the platform of Matlab.3.Taking the Fourier transform domain as the sparse domain and the non-uniform sampler as the compressed sampling model,the FISTA algorithm is applied to solve the BPDQ based optimization problem.A compressed sampling signal recovery algorithm is implemented which is easy to implement and can efficiently handle large-scale optimization problems,and is verified by simulation on Matlab platform.4.In order to reduce the complexity of digital circuit implementation,we first optimize the signal recovery algorithm based on the FISTA operation framework.Finally,we give the optimized design and implementation of FPGA recovery algorithm,and verify it by Vivado Simulator.5.The principle of adaptive threshold and the calculation method of PDW parameters of pulse signal are studied.The design and implementation of adaptive threshold and PDW computing module on FPGA are given.Finally,combined with the signal recovery module,the signal recovered after compressed sampling is taken as the input signal model,and is verified by simulation on Vivado Simulator.