Passive Direction Finding Key Technology And Algorithm Implementation

Passive direction finding technology is a method to detect the direction of the radiation source in a passive way.This method does not emit energy when detecting the target.With the collected information of the radiation source and the own information of detection station,applying appropriate algorithm can estimate the direction of radiation source.Passive direction finding technology widely used in military or civilian fields such as military warfare,traffic control,astronomical observation,and disaster relief and reduction.The DOA(Direction Of Arrival)estimation algorithm based on array signal processing has become an important research field of passive direction finding technology because of its high resolution,high estimation accuracy and strong antiinterference ability.Compressive Sensing theory is a very popular emerging technology in recent years.It can be used in passive direction finding to overcome many of the shortcomings of traditional direction finding algorithms.In this thesis,a variety of DOA estimation algorithms based on array signal processing are studied,and their estimation ability is verified by simulation analysis.The main research work and innovative contributions of this thesis are as follows:1.The basic theory required for passive direction finding technology is introduced.Firstly,the array signal model is given,then two commonly used algorithms of the number of signal source number estimation,feature decomposition method and information theory method are introduced.Finally,the basic principle of particle swarm optimization is introduced.2.The classification of the direction-of-arrival estimation method is introduced.Then the beamforming method and the Capon minimum variance method in the traditional algorithm are analyzed,and their advantages and disadvantages are analyzed by simulation experiments.Aiming at the problem that the resolution of these two directions finding algorithms is not high enough,the MUSIC algorithm and ESPRIT algorithm are studied,their basic principles are analyzed,and their algorithm steps are summarized.Then,the simulation experiments of these two algorithms are carried out,and the factors affecting their performance are analyzed,and their advantages and disadvantages are summarized.3.Aiming at the problem that subspace algorithms such as MUSIC and ESPRIT can not effectively resolve the direction of coherent signals,the algorithm for DOA estimation of coherent sources is studied.Firstly,the spatial smoothing algorithm and the modified MUSIC algorithm are introduced,and their basic principles and direction finding steps are analyzed.Then based on the idea of spatial smoothing,combined with the properties of signal subspace and noise subspace,an EM-MUSIC algorithm using a new spectral function is proposed.Then through the simulation comparison of these three algorithms,it is concluded that EM-MUSIC still has strong estimation ability under low SNR.4.Since the traditional subspace algorithm requires multiple sampling to obtain a large amount of snapshot data,it brings a large amount of calculation.In this thesis,an algorithm for reducing measurement data by compressive sensing technology is studied,then The particle swarm optimization algorithm is applied to improve the orthogonal matching pursuit algorithm.Finally,the simulation results verify the efficiency of this scheme.