The Research On Key Technology For Estimation To Direction Of Arrival On Array Signal Processing

Direction-Of-Arrival(DOA) estimation techniques in array signal processing with array antenna have wide applications in a variety of fields ranging from radar, communication, sonar, seismology to radio astronomy. Especially, they become a key technique in the passive detection of array radar, SDMA of smart antenna system and detection sound source. Since early 1980s, high resolution DOA estimation techniques have received considerable attention and a lot of significant processes have been achieved in this field. However, there are still important and urgent problems that have not been solved perfectly. The basic model and theory of array signal processing are introduced in this dissertation firstly, then, the properties and disadvantages of the existed DOA estimation are analyzed. Furthermore, the new algorithms are proposed and discussed.In actual situations, there are many moving sources, so dynamic DOA estimation becomes the important research topic, MLE has the excellence performance as a DOA method, it is a no linearity and multidimensional estimation, it take a long time to do, A new method to estimate direction-of-arrival (DOA) of moving sources is proposed. Making use of maximum likelihood algorithm, this method can avoid the decompositions of the covariance matrix which should be repeated in the methods based on subspace tracking. In order to solve the problem of the huge computation cost in maximum likelihood algorithm, the particle swarm algorithm was considered and improved. So the aims can be tracked and estimated in a very little space in which the maximum is searched for. In this way the searching place was reduced greatly and the swarm intelligence was used in searching, so the cost can be reduced mostly. Simulation results show that the DOA estimation based on the improved particle swarm algorithm has the ability to track coherent sources and performs better than the methods based on subspace tracking in the aspect of tracking precision with the ability of being real-time.The traditional algorithms always run into the supposition that the signal and the noise obey the Gaussian distribution, and obtain better results by using more than second-order statistics. In practical applications, much random signal and noise encountered is not Gaussian distribution, such as atmospheric and lightning noise, the instantaneous peak on communications line and a variety of man-made noise, in which these are many significant peaks and the traditional second-order statistics-based methods of treatment should not be satisfied. There is a very important statistical signal model known as the Alpha stable distribution, which can describe the above-mentioned noise. Therefore the text briefly introduced the stable distribution and the fractional lower order moment. This paper provides a new improvement on particle swarm optimization algorithm basing on the idea of locking and tracking, and study a new method based on the maximum likelihood algorithm for dynamic direction-of-arrival (DOA) estimation in impulsive noise environments. This method can avoid the decompositions of the fractional lower order moments matrix which should be repeated in the methods based on subspace tracking, and perform better than the methods based on subspace tracking in the aspect of tracking precision. In addition the cost of the multidimensional search can be reduced mostly.In order to overcome the low sensor utilization rate problem existing in most two-dimensional DOA estimation algorithms, a new array model with low array redundancy is proposed in this paper. Therefore, the application of MRLA is extended to 2-D DOA estimation. Simultaneously, two-dimensional spectral peak searching and Eigen-decomposition of large matrix is avoided by using propagator method. The computational complexity is greatly reduced. The larger array effective aperture is obtained by using MRLA, So the performance of DOA estimation in the poor environment with low SNR is obviously improved. Simulation results showed the superiority of this proposed method in precision. Based on virtual multi-element uniform linear array and reconstructed fractional lower order covariance matrix, a novel maximum likelihood (ML) algorithm is proposed. The proposed algorithm utilized few virtual elements and expanded the number of effective aperture array, while significantly improving the performance of the original ML algorithms, In order to fit the proposed direction finding algorithm based on the minimum redundant array and fractional lower order covariance matrix, a bee colony algorithm is applied to objective function of direction finding. Monte-Carlo simulations have proved that the proposed method has some good performance such as high resolution in the presence of impulse noise and the capability of using a small number of elements to find more signal sources. A new method for estimating two-dimensional direction-of-arrival based on special linear array was presented. The virtual array's rotational invariance can be got by the rotational invariance of the reference arrays, by using the method of two directions'expansion, the array's expanding ability can be increased, and it can also eliminate the redundant data of the forth-order cumulant matrix. By using this new method, the signal's subspace estimation can be obtained by only two forth-order cumulant matrix, and the estimation of the signal's DOA can be achieved by the 2-D ESPRIT method. The theoretical analysis and simulated results show that this method is characterized by low computation cost, well expanding ability,high precision and good practicability.In the wide-band direction-finding algorithms based on the signal subspace approach, the focusing matrix has an important effect on the performance of the estimation. This paper proposed a new method of constructing focusing matrix and the signal to noise ratio of the array before and after focusing was equal without any loss. New approach can distinguish the two targets which are close to each other even break the restriction of Rayleigh resolution limit and has higher accuracy compared to TCT algorithm while the signal to noise ratio is very low. Extensive simulation results demonstrate that the algorithm has good performance.