Study On Radar Emitter Signal Identification Based On Intra-Pulse Features

The recognition of radar emitter signals(RES) is a key technology in signal processing of radar countermeasures, and the recognition level of RES has become an important symbol of the technical merit of the radar countermeasures equipment. For a long time, traditional methods of recognizing radar signals are generally based on five conventional parameters, which these methods are effective and can obtain satisfactory results in the low dense environment. However, with the rapid development of electronic technology and radar technology, the modulation manner of RES became more and more complex and various, and the circumstance of RES became increasing denseness. As a result, the performances of these traditional methods descend rapidly. Therefore, only some new and valid approaches are explored to improve the technical merit of electronic countermeasure equipments.In recent years, though many scholars have helpfully explored a great of new methods to improve the recognition level of RES, these proposed approaches only analyze those signals in high SNR, obvious difference, fixed signal parameters, and fewer emitters. The existing methods are difficult to identify same modulation signals, parameter-changes signals and different modulation signals in low SNR. The feature-separability of radar signals and the singularity pulse processing in dense circumstance of RES are not also studied. In fact, the recognition technology of RES has been restricted by these problems. Thereout, from the view of four important facets, which are feature-separability, intra-pulse feature extraction, novel model and algorithm of signal recognition, recognition methods of advanced RES have been studied. The research fruits are as follows.1. In order to explore the problem of the radar signal feature-separability, the one-dimensional feature-separability model of the signal feature is built based on the probability and statistics theory. According to parameters obey approximate normal distribution, and the relationship sub-model of the correct classification probability and correspondence feature statistics parameters is proposed. As a result, the correct classification probability is more than 90% when the ratio value of the measure precision and the absolute value of the difference of two feature mean values is not less than 3.3. Afterwards, the separability measurement of convention parameters in signal recognition is gained.2. The feature extraction algorithms of ridge-frequency features and Cscade Cnnection features of the ridge-frequency feature are proposed. Via these algorithms, some new parameters can be extracted, so that different modulation signals can be recognized. According to the time-frequency principle and the definition of the ridge-line, the condition restriction model of the ridge-frequency feature is constructed, and then the improved ridge-line feature extraction algorithm is proposed based on a new wavelet atom and extraction strategy of ridge-line. After extracted the ridge-frequency of radar emitter signals, the Cscade-Cnnection features of the feature are extracted to describe the modulation characteristics of the signal. The results of classification experiments based on increment fuzzy support vector machine demonstrate that Cscade-Cnnection features of the ridge-frequency featrure can reflect the difference of different modulation signals, and have a good ability to resist noise.3. The wavelet packet fusion algorithm and the feature extraction algorithms of fusion entropy features are proposed to construct the effective recognition feature vector for approximately radar emitter signals(i.e., the modulation manner of the signal is identical, but some parameters of the signal are different). In this method, the choice rule of the wavelet is presented, and the fusion algorithm is reconstructed based on the wavelet packet decomposition and the principle component analysis. Similarly, the fusion Shannon entropy, fusion Norm entropy and fusion Probability entropy are extracted to describe the energy structure of the signal, and analyzing the resisting noise capability of three entropy features. Afterward, the parameters of LFM are estimated. Considering the number of wavelet decomposition layers, dimension numbers of the feature and various parameters, it is been researched detailedly that the recognition performances of the different features based on different recognition algorithms in this paper. The experiment results show that the proposed approach not only achieves good in recognition effect, but also suffers less computational burden than traditional methods.4. According to analyses of the nonlinearity of the radar power amplifier, the harmonic power restriction model is constructed to describe the nonlinear characteristics of the amplifier, and the correspondence feature extraction algorithm of harmonic power restriction (HPR) is proposed. Via this algorithm, some unintentional-modulation features are extracted to recognize the emitter. In this algorithm, the correlation estimation model of the harmonic power of the signal is proposed based on two-term formula. Comparing with the solidity of the HPR feature in different power conditions, the linear relationship of HPR features is obtained when input power of the amplifier is various. The experiment results have shown that these conclusions can be drawn in this paper, if energy accumulation of the pulse signal is enough.5. Aiming at unknown radar signal processing, lower signal recognition rate and longer training time of the existence signal recognition algorithm, an increment fuzzy support vector machine algorithm is proposed to improve the signal recognition rate, and the correspondence theory and solving-scheme are studied detailedly in the design of the algorithm. In this algorithm, the combine membership function of every training example and the support vector fuzzy data description method for confirming the radius of the hypersphere are proposed, the conception of the common training data set is introduced and the increment fuzzy training algorithm is also presented. Then, in order to control the loss alert ratio, the rejection strategy of unknown radar emitter signals is proposed based on the attribute theory. Lastly, considering all kinds of effect factors, it is been studied deeply that the recognition performances of the recognition algorithm based on different parameters and training example numbers.This work is supported by the National Natural Science Foundation of China (No.60572143, No.60702026) and the National Electronic Warfare Laboratory Foundation.