Research On Phased Array Radar Behavior Recognition And Reasoning Technology

With the increasing position of electronic warfare in the future military field,electronic reconnaissance as a pioneer of electronic warfare becomes increasingly important.In the actual battlefield environment,through the use of electronic reconnaissance methods,it is often possible to obtain enemy radar radiation source information in advance,so that it can quickly formulate corresponding military strategies and take actions for protecting,greatly improving ability of the threat warning and the military defense.However,in the perspective of traditional electronic reconnaissance methods,most of the previous electronic reconnaissance methods are based on the static information of individual radiation sources,and they could not analyze the behavior properties and intentions of radiation sources in depth to achieve the dynamics of radiation sources.The extraction of information provides strong support for subsequent deployment of combat situations.In view of the above problems,the main work of the paper is as follows:1.The behavioral theory of phased array radar is studied,and the modeling and simulation of the behavioral model and its characteristic analysis are accomplished.First,from the basic principles of the phased array radar system,the system composition structure of the phased array radar is explored,and the multiple working modes of the phased array radar in multiple states such as search,compound,and tracking are detailedly analyzed;Secondly,basing on the typical battle scenario,modeling and simulation of various working modes are accomplished;Then,basing on the phased array radar resource scheduling strategy,the switching criteria of various working modes are explored;Finally,through the analysis of the working principles of various patterns,a set of pattern description words for subsequent behavior recognition and reasoning are proposed.2.The behavior recognition technology of phased array radar is studied.First,according to the respective advantages of the enhanced algorithms and support vector machine in the machine learning algorithm,the algorithm fusion of the both algorithm is accomplished.And basing on the Ada Boost-SVM fusion algorithm,an optimal pattern recognition model under the different conditions of the noise ratio is put forward.Then,through searching and optimizing MLP network and a large number of combination experiments,an optimal network architecture for various types of work pattern recognition is proposed.And a good pattern recognition effect is achieved.3.The behavioral inference technology of phased array radar is studied.First,basing on the ability of long-term and short-term memory network to store long-term sequences,and by exploring the sensitivity of multi-layer LSTM networks to deep variable characteristics in time series information,a network architecture that can be used for behavior pattern reasoning is proposed;With the advantages of one-dimensional CNN for feature extraction of time series,and the semantic conversion characteristics of the Encoder-Decoder framework,and the focus characteristics of the important feature of the Attention mechanism,through further integration and improvement of the LSTM network,a hybrid network architecture is finally constructed.And the experiments verify that the hybrid network architecture has better abilities to predict behavior intention than other network structures.