Research On Fast Sorting Recognition Technology Based On Known Signal Samples And Signal Knowledge

Matching signal processing is the basic design criterion in signal processing for radio equipment,with the output of the largest SNR.The basic condition of radar matching signal processing is to know the self-transmitted signal and use this as a reference to process the received signal.Radar reconnaissance can intercept radar transmit signal samples and estimate its signal modulation parameters,but the intercepted signal samples always contain internal and external noise,and the modulation parameter estimation can only achieve limited precision.The radar signal has certain constant return characteristics.In the continuous reconnaissance process,It is the goal of this research whether the acquired radar signal samples or modulation parameters can be used to quickly and accurately detect and sort out the pre-intercepted known signals from the large number of dense signal samples that arrive later.Therefore,a new concept of signal detection,filtering and sorting based on priori information of non-cooperative signals is proposed in the thesis.Not only the non-cooperative signal information acquired during the radar reconnaissance process is build,but also uses this information to construct the best or quasi-optimal signal processing channels of the signal detection,filtering,and sorting,in order to improve the sensitivity of the signal processing,accuracy of parameter estimation,reliability,and speed of processing.Firstly,in order to solve the problem that the signal samples contain noise and the estimation accuracy of the modulation parameters is low and cannot be used for matching signal processing,a quasi-best matched filtering detection method based on known signal samples and signal knowledge is proposed.The method can quickly detect and identify the known signals and unknown signals by designing a quasi-matched filter based on the known signal samples in the database in this thesis,thereby greatly improving the speed of signal sorting recognition.In this thesis,the detection and recognition of signals are realized by using the quasi-matched filtering algorithm.The quasi-matched filtering refers to the quasi-optimal filtering detection using the acquired noisy waveform data.In this thesis,the principle of quasi-matched filtering is derived,and by comparing the matched filtering output signals and quasi-matched filtering output signals,it is proved that the quasi-matching filtering has good signal detection performance when the sample SNR is high.The importance of continuously learning and improving the SNR of sample signals is demonstrated by the simulation experiments of three typical radar signals under different sample SNR conditions.Then,aiming at the problem of poor performance of quasi-matched filtering detection through incomplete signal samples,this thesis presents a signal identification method based on the frontier envelope of pulse signal according to the robustness and stability of the frontier waveform of pulse signal.The method realizes sorting of the known signals and unknown signals by the similarity between the frontier envelope waveform of the signal to be processed and the frontier envelope waveform of the signal sample,thereby improving the speed and flexibility of signal processing.The proposed method has better detection performance when the SNR is high is showed by simulation results.Since the detection capability of the quasi-matched filtering method based on known signal samples is closely related to the SNR and integrity of the signal samples,it is very important to research the sample establishment method.For the accuracy of signal sample build by two sample establishment methods based on signal parameters and signal waveforms can't satisfy the requirement of the sample database establishment.A new method combining sample parameters and signal waveforms is proposed in this thesis,and the specific application method of the chirp signals and phase coding signals is given.The method is most effective is proved through the situation experiment results when the parameter sample is inaccurate.