Research On Uav Monitoring Technology Based On Array Direction Finding In Railway Transportation

As Unmanned Aerial Vehicle(UAV)plays an increasingly important role in many industries such as agriculture,transportation,and power,the frequency of drones appearing around us is also increasing,and the risks that accompany them are gradually increasing..In recent years,there have been many incidents of drones interfering with the normal operation of high-speed rail.In order to quickly and effectively find out where the drone signal comes from and realize the accurate direction finding of the drone signal source,it is a new research topic for rail transit safety protection.Firstly,this dissertation introduces the array signal model estimated by Direction of arrival(DOA),and gives the assumptions of the conventional array signal model.Based on the array signal model,the uniform linear array model and the uniform circular array model are analyzed,and the array receiving model of the signal is analyzed under the condition of a coherent source..Secondly,this dissertation introduces the DOA algorithm based on spatial spectrum estimation.This type of algorithm has super-resolution and mainly represents the Multiple Signal Classification(MUSIC)algorithm and the estimating signal parameter via rotational invariance techniques(ESPRIT).After analyzing the basic principles of these two algorithms,this dissertation analyzes their resolution and direction finding accuracy.MUSIC algorithm and ESPRIT algorithm have good estimation performance when estimating uncorrelated sources.However,when the signal sources are coherent,the DOA estimation performance of these two algorithms is in a state of failure,and the angle information of the coherent sources cannot be obtained.Then,in order to solve the problem that the MUSIC algorithm and the ESPRIT algorithm cannot perform effective DOA estimation on the coherent sources,the traditional DOA estimation algorithm is analyzed for the cause of the failure of the coherent sources,and the traditional coherent space smoothing algorithm is explained.Coherent performance.Based on the spatial smoothing algorithm,two improved decoherence algorithms are given: the quadratic weighted spatial smoothing MUSIC algorithm.This algorithm uses the nested use of the spatial smoothing algorithm to obtain a weighting matrix and smoothes it to make full use of the auto-correlation and cross-correlation information output by the sub-array,improve the orthogonality of the signal subspace and the noise subspace,and have a better estimation effect on the direction of arrival of coherent sources;improve the weighted space smoothing TLS-ESPRIT algorithm,The modified algorithm divides the original array into sub-arrays with a special structure,and directly uses the auto-correlation and crosscorrelation information output by the sub-array to obtain a weighting matrix,thereby achieving decoherence of coherent sources.Finally,for the drone signal,considering the size of the direction finding equipment and the real-time performance of direction finding,the DOA algorithm of the drone direction finding software designed in this thesis uses the relevant interferometer algorithm.This thesis introduces the basic principle of the phase interferometer algorithm,and analyzes the phase ambiguity in the direction finding process.Based on this,the basic principle of the correlation interferometer algorithm is analyzed,and the phase sample bank of the signal is constructed by the cost function,and the phase ambiguity problem in the phase interferometer algorithm is solved.The correlation interferometer algorithm is not affected by the environment.Under the adverse conditions of mutual coupling and noise,it can also have good DOA estimation performance for signals.