JEM Characteristic Extraction With Wavelet Analysis And Research On GA-BP Algorithm

Because of the influences of some inherent performances, such as low repeat frequency, it is always very difficult to apply convention air defense radars to airplane goal automatic sorting and recognition in the target identification research area. At present, massive convention air defense radars are equipped in domestic. Obtaining the breakthrough in the convention air defense radar's target identification will have momentous military and economical significance. Extracting valid airplane goal characteristics exactly and rapidly from the echo and multi-sensor information fusion are two pivotal techniques. The prime tasks of this paper are airplane periodic signature of modulation extraction with wavelet decomposition as well as structuring intellectualized type fusion model and algorithm of target identification.Because the airplane's revolving part can have cyclical modulation feature to the radar electromagnetic wave modulation, convention air defense radar could extract periodic signature of modulation to distinguish airplanes of different types effectively. There are some disadvantages in conventional extract methods, such as differential cepstrum analysis, estimate of unbiased autocorrelation, AR power spectrum and so on. Some need high radar repeat frequency, some have biggish error and some have to do a mass of computation. According to the research of modulating feature parameter model of helicopter, propeller-driven aircraft and turbofan jet airplanes'echo, in this paper, we analyze the echo signal feature and put forward the algorithm of extracting target echo's periodic signature of modulation by wavelet analysis. In this paper, we also discuss the question of how to choose appropriate wavelet function and the influence of several radar parameters to the result of extract in detail and give corresponding results and conclusions of simulations.In the aspect of multi-sensor information fusion, a multistage neural network fusion system based on neural network, fuzzy reasoning and expert system will be used. The neural network includes sensor subnet and fusion subnet. The sensor subnet obtains target feature informations through many sensors and gives the likelihood of every target type. The fusion subnet unifies each sensor's confidence level to fulfill fusion task with the output results of sensor subnet and finally distinguishes the type of target. This paper puts emphasis on the model and algorithm of sensor subnet of the multistage neural network fusion system. The sensor subnet is one kind of fuzzy neural network based on expert rules. The architecture and each node of the network all have accurate meanings. Also in this paper, the algorithm of the sensor subnet will be optimized, the genetic algorithm optimized and back propagation algorithm will be fused to obtain a new algorithm named GA-BP algorithm which has more excellent performances. At last the sensor subnet will be trained and tested with this algorithm.