Research On Direction Finding For Coexisted Uncorrelated And Coherent Sources

Direction of arrival (DOA) estimation is an important area in array signal processing,and has widely application in the field of military and civilian, such as radar, passive sonar,radio astronomy, communication, etc. During the past five decades, scholars at home andabroad have proposed a series of high resolution direction finding algorithms. And they havemade significant achievement in improving the performance of parameter estimation. Withthe development and application of electromagnetic technology, the density of signalsincrease greatly, and the uncorrelated and coherent signals always coexist. In this situation,the traditional DOA estimation algorithms fail or degrade since they can’t make full use of thearray aperture. In this paper, we focus on the direction finding issue for coexisted uncorrelatedand coherent signals, aiming to exploit the array aperture efficiently, and improving theperformance of estimation.The main idea of DOA estimation for coexisted uncorrelated and coherent signals is toestimate the DOAs of the uncorrelated and coherent signals separately. In this way, the arrayaperture could be fully ultilized, and the performance of estimation will be improved. DOAestimation for coexisted uncorrelated and coherent signals is comprised of three key parts: theestimation of uncorrelated signals, the separation of coherent information, and the estimationof coherent signals. In this paper, we concentrate on investigating new methods to separatethe coherent information and estimate the coherent signals. The main work of this paper islisted as follows.Firstly, we study the direction finding problem for coherent signals. For uniform lineararray, by exploiting the eigenvector corresponding to the big eigenvalues as well as itsbackward vector to construct the decorrelation data matrix, the improved vectorreconstruction method is proposed. It has better performance than the common methods forboth incompletely and completely coherent signals. For arbitrary array, we present a signalsubspace measurement model by regarding the signal subspace as compressed sensingmeasured vector. It can improve the DOA estimation performance of the single measurementvector model and multiple measurement vectors model greatly at low signal to noise ratio.Theoretical analysis and simulation results illustrate the good performance of the proposedmethods.Secondly, we discuss the one-dimensional DOA estimation problems for coexisteduncorrelated and coherent signals. For uniform linear array, a new derection finding algorithm is proposed using the Toeplitz property separation method, the root-MUSIC algorithm as wellas the improved vector reconstruction method. Comparing with the similar algorithms, theproposed algorithm has higher array aperture and better performance of estimation. Foruniform circular array, the mode space transform technique is adopted, so that the algorithmfor uniform linear array could be implemented. This algorithm can cut down the computationcomplexity for coherent signals greatly and make up for the loss of array aperture. Forarbitrary array, the oblique projection technology and signal subspace measurement model areemployed to separate the coherent information and estimate the DOAs, respectively. Theproposed algorithm can expand the array aperture, and has much smaller computational loadthan the common DOA estimation algorithms for arbitrary array. Moreover, this algorithm ismore suitable for the practical application of engineering, which is of great practicalsignificance.Thirdly, we research on the two-dimensional DOA estimation algorithms for coexisteduncorrelated and coherent signals. For L shape array, direction finding algorithms based onthe eletromagnetic vector sensor array and the scalar sensor array are proposed, respectively.Based on the one-dimensional DOA estimation algorithms of uniform linear array, the twoalgorithms make the following improvement. DOA estimation algorithms with smallcomputational complexity and good performance are chosen. Effective parameter pairmatching methods are proposed respectively. New decorrelation methods are proposedrespectively. Comparing with the similar algorithms, these two algorithms have higher arrayaperture and better performance. For arbitrary array, the space-time processing technology isimplemented, so that the rotational invariant relationship could be constructed to simplify theprocess of estimation. Moreover, by constructing the data matrix skillfully, the information ofthe uncorrelated and coherent signals could be separated according to their characteristics, andthe parameters are paired automatically. This algorithm cut down the computation load for thearbitrary array greatly. Theoretical analysis and simulation results illustrate that this algorithmhas great potential in expanding the array aperture.Finally, we consider the DOA estimation in impulsive noise. And new direction findingalgorithms are proposed based on the second order statistics by employing the preprocessingmethod to weaken the impulsive noise. Since the impulsive noise doesn’t have second or highorder moment, the DOA estimation algorithms in impulsive noise are mostly based on thefractional lower order statistics. By estimating the amplitude of the signals and normalizingthe amplitude of the array received data, the strength of impulsive is weakened. In this way,the algorithms proposed before could be applied to impulsive noise. Simulation results illustrate that the proposed algorithms have small computation complexity and betterperformance than the algorithms based on the fractional lower order statistics, and is effectivein strong impulsive noise, too.