A Study Of Nonlinear Method For Specific Communications Emitter Identification

In the field of communications countermeasure, there are two tasks known as positionand recognition. As a main research direction, position technology been used widely. But theresearch on recognition technology is not enough relatively. Position can solve the problem of“where”, whereas recognition will resolve the problem of “what”. A more effectivecommunications reconnaissance must be the combination of these two technologies. Now theresearch on recognition technology is more urgent because position has been widely applied.The military initiative position would be taken in the complicated electronic warfareenvironment, if an important communications radiation source is recognized with its tinyfeatures and its identity is known.In this paper, pre-processing, pattern separation and feature extraction forcommunications steady signals have been studied. With MATLAB, Models of unintentionalmodulation signals are built to generate simulating signals used to verified algorithms ofpattern separation and feature extraction. Then these algorithms were used to extract thefeatures of10Kenwood FM tow-way radio signals, which were labeled by ECOC multiclassfier. The results showed these algorithms can extract identity feature. The author’s majorcontributions are outlined as follows:1. Unintentional modulation introduced by oscillator has been studied. According to thestatistic characteristic of oscillator noise, a noise model was built with MATLAB. Generationsof not only flicker noise, but also flicker noise plus Gaussian white noise were simulated forresearch on time waveform and frequency spectrum. Then real signals acquired from aKenwood tow-way radio were analysised, which show its spectrum is obeyed power lawwhich indicated there was flicker noise.2. Unintentional modulation induced by RF power amplifier has been studied. Accordingto the nonlinear theory of RF power amplifier, a memory polynomial model was constructedand simulated for QPSK and WCDMA signals in MATLAB. ACPR values of simulating datawere computed. It can be seen that the wider the bandwidth was, the lower the ACPR was.The effects caused by amplifier on signals were variations of amplitude and phase. Becausethese variations carried information of power amplifier, so they can be used to recognizecommunications radiate source.3. Algorithm of pre-processing signals has been studied. The algorithm of noiseseperation proposed by Wornell and Oppenheim was improved for filtering hybridunintentional modulation signals from noise combined signals. Multi objects optimizationalgorithm was used to solve nonlinear equations to acquire parameters of fractal signals. Then these parameters were used to construct a fractal filter. After wavelet coefficients processed bythe fractal filter and were transformed to time domain, white noises were removed fromsignals. Cases of different SNR, data length were simulated that indicated recovered signalshad low RMS error if length of data was enough.4. Three feature extraction methods have been studied.(1) Fractal method changed1-Ddata into2-D to expose texture of signals. Then fractal dimension spectrum of2-D data arraycan be computed and used as feature vectors. The simulation showed that nearly samefeatures can be acquired if data were pre-processed before features were extracted.(2) Orderstatistic method sorted data that have been whited. Then processed data were a line which wasthe monotone function about power amplifier input-output. Features can be extracted withfunction fitting.(3) Research on high order cumulants tensor method derived the relationbetween3,4order cumulants and identity features. By kernel PCA method, features can beextracted from3order tensors transformed from4order cumulants. At last, ECOC classifierwas used to recognize these features. Results declared that these methods can extract featureseffectivly.This paper improved the research on the recogniztion of communications radiate source.Methods of extracting features from steady communications signals were explored andverified. These methods may be used to recognize identity of communications radiate sourcein the non-cooperative communication environment and enrich current available methods.