Analysis And Recognition Of Pulse Repetition Interval Pattern For Radar Pulse Train

Pulse repetition interval(PRI)analysis of radar signals is an important task in electronic intelligence(ELINT)analysis.With the development of radar technology and electronic industry,the radar signals faced by ELINT system are becoming more and more complex,and the requirement of PRI analysis is becoming more and more urgent.The current PRI analysis theory and method based on low dimensional PRI statistics can hardly meet the requirement.Recently,the preliminary algorithms by deep mining PRI pattern of radar signals are hopeful to solve the above problems,and show that have great advantages in processing complex radar signals.Therefore,the PRI pattern analysis and recognition of radar reconnaissance pulse trains are studied in this paper.Firstly,signals of conventional radars occupy a large proportion in most radar reconnaissance datasets.Automatic and accurate analysis of the PRI patterns of these radars is an important task in realizing intelligent processing of radar reconnaissance data.Therefore,a PRI patterns analysis method of conventional radar pulse train is proposed in this dissertation.In order to reconstruct PRI pattern of each radar emitter in the interleaved pulse train without deinterleaving,this method introduced the frequent item mining technique of the IS measure to analysis the radar reconnaissance data.The simulation results showed that the method had strong ability of anti-missing and anti-interferential pulses and certain adaptability to the number of emitters.With the increasing application of multi-function radars,the PRI pattern analysis of multi-function radar signals can’t be ignored.Reconstructing the PRI patterns of multi-function radar is faced with great challenges,because multi-function radar signals usually have more complex hierarchical structure compared with conventional radars.This dissertation proposes an PRI pattern analysis method of multi-function radars based on frequent items and contextual entropy.Pulse sequences are dimension-reduced processed to pulse subgroup sequences.Combined with natural language word segmentation technique of contextual entropy,the PRI patterns of multi-function radars can be extracted successfully from the pulse subgroup sequences.The proposed method has strong adaptability to data noise,and provides priori information and basis for subsequent sequential recognition of PRI patterns of multi-function radars.Finally,taking pulse Doppler radar as an example,this dissertation studies the sequential recognition of PRI patterns of multi-function radars,and proposes a sequential recognition method of PRI patterns based on regular grammar and finite automata.This dissertation addresses the sequential recognition problem based on the prior information of radars’ pulse group provided in the database of the electronic support system.Through the regular grammar of multi-function radar PRI patterns,the proposed method establishes the hierarchical automata to sequential recognize the PRI patterns from radar reconnaissance pulse trains.The hierarchical automata used for PRI pattern recognition has a two-layer structure.The bottom layer of the hierarchical automata realizes the sequential input of pulses and recognition of pulse subgroups,and the sequential input of pulse subgroups and recognition of PRI patterns are realized at the top layer.In the face of multi-function radar signals with complex temporal structure,the proposed method has good adaptability,and by expanding the scale of automata,it will be able to recognize more complex PRI patterns.