Research On Ultra-high-speed Target Detection And Recognition Based On External Radiation Source Rada

Compared with traditional single-base radar,external emitter radar has the advantages of good concealment and strong anti-interference.Based on airborne external emitter radar,this paper studies the methods of ultra-high-speed target detection and recognition.The main work is as follows:(1)For the echo signal received by the main antenna of airborne receiving station contains strong direct wave,which will have an adverse effect on target detection,the principle of direct wave cancellation by adaptive filtering is introduced.This paper studies the least mean square error algorithm,recursive least square method,block frequency domain adaptive filtering algorithm commonly used in engineering at present,and compares the direct wave cancellation performance,convergence performance and algorithm complexity of these three algorithms through MATLAB simulation,analyzes their advantages and disadvantages.(2)For ultra-high speed target can’t use the conventional detection method of the distance and speed for accurate measurement of this problem,this article on the basis of the existing target detection algorithm presents a target detection algorithm based on multiple speed gear processing,and making use of the MATLAB simulation respectively under ideal conditions and environmental noise performance of the algorithm.Simulation results show that the algorithm can realize the coherent accumulation of target echo and estimate the distance and velocity of ultra-high speed target accurately.(3)In practice,there are some decoy targets around the ultra-high speed target to interfere with our radar’s detection and recognition of the real target.In this paper,a fretting model of the target is established,and the theoretical analysis and characteristic simulation of the micro-Doppler frequency shift of the real target and the decoy target are carried out.Then,the micro-Doppler difference of the real target and the decoy target is extracted by the time-frequency analysis algorithm of the SHORT-time Fourier transform.Multiple sample data were used to extract the motion characteristics of true and false targets through principal component analysis(PCA),and support vector machine algorithm(SVM)was used to verify the classification of true and false targets,which achieved high recognition success rate.In general,the target detection algorithm based on multi-velocity profile processing and feature extraction and classification recognition algorithm based on machine learning proposed in this paper can accurately estimate the distance and velocity information of hypervelocity targets and distinguish true and false targets,and basically complete the project research on hypervelocity target detection and recognition based on external radiation source radar system.