Research On Working Pattern Recognition And Behavior Intent Prediction Of Airborne Phased Array Radar

The identification of airborne phased array radar's working mode and prediction of its behavior intention are the direct basis of threat level identification and countermeasure strategy selection,as well as the important research content of cognitive electronic warfare.However,with the development of phased array technology,airborne phased array radar has developed into a highly intelligent sensing system with the ability of parallel execution of multiple operating modes.Its strong flexibility and adaptive ability bring unprecedented challenges to radar countermeasure technology.Therefore,how to identify the operational mode and reason the behavior intention of airborne phased array radar has become an urgent research topic in the field of electronic warfare.In this paper,working pattern recognition and behavior intention prediction of airborne phased array radar are studied.Based on the analysis of different working modes of airborne phased array radar,the algorithms for working pattern recognition and behavior intention prediction of airborne phased array radar are presented respectively.This paper mainly includes the following three aspects:Firstly,from the scanning principle of phased array radar,the system composition,resource management and task scheduling of phased array radar are introduced.After the mission profile of typical air-to-ground combat scenes,the characteristics of different operation modes such as search and tracking in time domain,space domain and frequency domain of phased array radar are deeply analyzed,based on which feature parameters are extracted from the detection signals,and then applied to the recognition of operation modes.Secondly,the working pattern recognition technology of airborne phased array radar is studied.On the one hand,the extracted radar signal features are innovatively represented by graph structure,and the GCN network model based on semi-supervision is constructed for the recognition of working modes,which shows that the model has good robustness under different SNR conditions.On the other hand,the characteristic parameters of radar signals in different working modes are processed by feature engineering to reduce the noise and reduce the network size.Then,the working pattern recognition model based on PCA-MLP is constructed.Compared with the traditional multi-layer neural network,the model improves the recognition rate by 0.23% while reducing the training time by 0.05%.Finally,the behavior intention prediction technology of airborne phased array radar is studied.In view of the problem that the working mode of phased array radar is very varied,the method of artificial neural network is considered to mine the switching rules of different modes.After introducing the cyclic neural network and its variants,LSTM and GRU,and analyzing and comparing the advantages of different networks,the behavior intention prediction model based on GRU network model is established.After optimizing the parameters of the network model,it can reduce the training parameters by 25.00% compared with the LSTM network,and has a more stable prediction accuracy and performance advantage.