Compressed Sensing DOA Estimation Based On Dictionary Optimization

Since the angle of the radiation source cannot be greatly changed in a short time,the azimuth angle information becomes the most stable parameter of the radiation source signal sorting.Therefore,the direction measurement of the radiation source is an important content of electronic reconnaissance,and the DOA estimation also becomes an important research topic in the field of electronic reconnaissance.The introduction of compressed sensing theory brings a new perspective to the research of DOA estimation.This paper begins with the criterion of compressive sensing sparse signal reconstruction and searches for a method of optimizing DOA estimation within the framework of compressed sensing.The main research work of the dissertation is:1.A compressive sensing DOA estimation model including a linear array,a circular array,and an L array was established.The linear array is a one-dimensional DOA estimator that estimates the angle between the signal and the array.The circular array and the L array perform two-dimensional DOA estimation for both azimuth and elevation.The DOA estimation model parameter settings and solution schemes under different arrays are discussed separately.The experimental verification and performance analysis of compressed sensing DOA estimation under these several array types are performed respectively.The OMP algorithm and threshold iteration are compared and analyzed.Two sparse signal reconstruction algorithms.2,Established a dictionary optimization model.Starting from the theory of compressed sensing,this paper seeks to optimize the method of giving compressed DOA estimation in the framework of compressed sensing theory.The core solution of DOA estimation based on compressed sensing is spatial sparse signal reconstruction.However,in compressed sensing theory,whether sparse signals can be successfully reconstructed has its own criteria,if it can improve sparse signal reconstruction accuracy from compressive sensing theory,At the same time,it also improves the quality of DOA estimation results.In this paper,the compressive sensing sparse signal reconstruction criterion is taken as the objective function,and a dictionary optimization model is proposed.It is determined that the orthogonality between the columns and columns of the dictionary matrix is used as the evaluation function,the position of the array elements is the optimization variable,and the genetic algorithm is used as the optimization process of the algorithm is optimized,and it is verified through experimental simulation that the dictionary optimization has a significant improvement in improving the performance of the DOA estimation algorithm.3.The introduction of signal vectorization model based on dictionary optimization further improves the performance of the algorithm.Based on the vectorization model,the virtual array element is added,the array aperture is expanded,the freedom of the array is improved,and the performance of the DOA estimation algorithm is significantly improved.Computer simulation experiment results show that the signal vectorization model can improve the performance of the DOA estimation algorithm.4.The performance of the dictionary optimization is analyzed.The performance difference between the optimized and pre-optimization DOA estimation algorithm is analyzed and compared.The effectiveness and feasibility of the dictionary optimization are confirmed.The performance of the optimized DOA estimation algorithm is analyzed under different signal source forms,different SNRs,different amplitude errors,and direction of incoming wave.It is verified that the dictionary-optimized DOA estimation model established in this paper has high accuracy and accuracy.High stability characteristics.