Research On Cluster Cooperative Intelligent Reconnaissance Technology

Electronic reconnaissance is the key support for information warfare.The electronic intelligence of the multi-function radar obtained through electronic reconnaissance activities provides intelligence information support for threat assessment and interference decisionmaking.On the one hand,the cluster cooperative reconnaissance system provides a new method for solving the shortcomings of single electronic reconnaissance equipment radar signal sorting in the field of electronic reconnaissance;on the other hand,some machine learning methods have significant advantages in extracting and analyzing the deep features of massive data.The intelligent reconnaissance and identification of the working mode of the multi-function radar provides a new idea.This thesis focuses on the two key issues of signal sorting under the coordination of multi-functional radar clusters and intelligent reconnaissance and identification of multi-functional radar working modes.Firstly,a model description of the multi-function radar is carried out.First,the physical simulation model of the multi-function radar is established from three aspects: phased array antenna pattern,wave position arrangement design,searching and tracking waveform unit design;"Unit/state/function" divides and describes the hierarchical structure of the multifunction radar signal,and establishes a theoretical basis for the intelligent reconnaissance and identification of the multi-function radar working mode.The two core issues of electronic reconnaissance of multi-function radar signal sorting and working mode reconnaissance and identification are discussed,the signal sorting framework and method of traditional radar signals are introduced,The shortcomings of the traditional single-station PRI sorting method and the multi-function radar working pattern recognition method are analyzed.Secondly,the multi-function radar signal sorting under cluster coordination is studied.First,a mathematical model of time-difference selection based on dual-station cooperative radar signal sorting is established to analyze the time-difference characteristics of radars of different repetition frequency types.The problem of increasing and missing batches is existed;second,the radar signal sorting under the multi-station cluster coordination is studied.Based on the principle of multi-station collaborative radiation source location,a false time difference elimination method based on the TDOA/FDOA weighted least squares algorithm is proposed,which effectively eliminates the false time difference peaks and histogram noise existing in the double-station cooperative sorting to improve the sorting accuracy,and it lay a foundation for the follow-up research.Finally,the intelligent reconnaissance and identification of the working mode of the multifunction radar is studied.The characteristics of the four classic working modes of TWS,TAS,STT and MTT are analyzed.Two kinds of neural network structures,namely lightweight CNN and CAE,are designed for the waveform unit/amplitude data of the four working modes.The characteristics of the amplitude/waveform unit data of different working modes are extracted to achieve the purpose of intelligent reconnaissance and identification of working modes,and then a fusion framework of intelligent reconnaissance and identification of multi-function radar working modes based on DS evidence theory under cluster coordination is proposed.Under different reconnaissance conditions,the performance of the two intelligent reconnaissance and identification algorithms and the intelligent reconnaissance and identification fusion framework under different reconnaissance conditions.This thesis takes the electronic reconnaissance of multi-function radar as the research object,and studies the sorting of multi-function radar signals under cluster cooperation.Based on the model,exploring an accurate and efficient multi-function radar working mode cluster collaborative intelligent reconnaissance and identification model has certain practical reference significance.