Research On The Signal Sorting And Recognition Based On Information Of AIC

With the rapid development of electronic technology, electromagnetic environment becomesmore complex. Modern communication developed to the direction of high frequency because theexisting communication systems are difficult to achieve the goal of anti-jamming or anti-listeningcompletely in the communication process for communicating parties and coupled with the limitedspectrum resource. So some new communication systems emerged, such as high-speed widebandfrequency hopping, burst signal, etc. They were applied to the field of military and civilian quickly,because they can overcome the drawback of serious interference, poor anti-listening and multi-pathfading. But the Nyquist theorem points out that the sampling rate should be more than twice of thesignal bandwidth in order to recover the original signal without distortion. Therefore, it needs highsampling rate and fast data processing speed when process high-speed wideband frequency hoppingsignal. It brings huge pressure to the existing hardware and restricts the development ofultra-wideband signals. So how to break the limitation of Nyquist theory and seek for lowersampling rate have become an urgent problem.In recent years, foreign scholars have proposed a new theorem named compressivesampling(CS). This theory indicates that the sampling rate is well less than Nyquist rate when signalpasses the analog to information converter(AIC). So it can solve the problem of front ADC needshigh speed sampling rate and huge pressure on storage device. After the sampling of AIC, itbecomes information domain data, not the traditional time domain data. So conventional signalprocessing method is no longer feasible, it is necessary to search for new rapid processingalgorithms. This paper have done some research on signal sorting and recognition based on AICinformation through combining the compressive sampling theorem and proposed severalinformation domain data processing algorithms which can be used in communication systems justlike UWB signal, frequency hopping.There are four parts in this paper. PartⅠ:From the perspective of discrete signals, this papermakes a full introduction about basic principles and implementation of compressive sampling. Thendo some in-depth study on spare decomposition of the signal, design of the measurement matrix,reconstruction algorithms. PartⅡ:The article construction three AIC structures: parallel type,segment type and pre-modulation type transition from discrete signals to continuous analog signals.It dose some simulations and analyzes the impact of input signal to noise ration which signalreconstruction needed and minimum measurement points under different AIC structures andparameters. PartⅢ:It describes several signal reconstruction algorithms based on signal reconstruction model of AIC information. This paper presents an adaptive compressive samplingmatching pursuit(ACSMP) algorithm. The algorithm can get rid of the dependence on sparsity, andcan approach the original signal through adjust the step size adaptively in iteration. The simulationresults show that the proposed algorithm can reconstruct the signal accurately, the probability ofreconstruction and the computational complexity are better than others. PartⅣ: It proposes minimum meansquare error algorithm to achieve the goal of fixed frequency suppression and compares withconventional algorithms. Because of different frequency of frequency-hopping signals can productdifferent reconstruction dictionary, this paper presents an algorithm to achieve the aim of sortingfrequency-hopping signals through solve the minimum sparsity of sparse vector by the informationof AIC. In the filed of frequency estimation about frequency hopping signal, the simulation resultsof subspace angle algorithm display that this algorithm can well estimate the frequency offrequency hopping signal under different signal to noise ration and the feasibility of this algorithm.