Research Of DOA Estimation In Impulsive Noise Environment

The estimation of direction-of-arrival (DOA) is an important area in array signal processing. The methods based on array signal processing can estimate DOA of several sources in different directions simultaneously. Conventional DOA estimation algorithms are mainly proposed for the Gaussian noise. But in reality, there is a large number of non-gaussian distribution noise, such as atmospheric noise, sea clutter noise, man-made noise, they are impulsive noise. So DOA estimation in the impulsive noise enviorment becomes the important research topic.The impulsive noise can be modeled as a stable distribution.This paper is divided into two main tasks. Firstly, a new method based on minimum-redundancy linear array and new fractional lower order covariance is proposed for one-dimensional independent signal sources. The method used the characteristic that the optimal minimum-redundancy linear array can fully expand the aperture of array and new fractional lower order covariance matrix is toeplitz matrix. It can estimate the sources number more than the array number. Simulation results show the method is effective. New fractional lower order spatial-temporal matrix is improved to be used in DOA estimation and a new spatial-temporal and maximum likelihood method based on fractional lower order statistics is studied to be used in DOA estimation for coherent sources. The new method avoids the drawbacks of sensor loss of space-smoothing algorithm. AP algorithm is used to solve the complex calculation of the maximum likelihood algorithm. Simulation results show the method is effective, especially in the case of low SNR.A new two-dimensional DOA estimation method based on quantum genetic algorithm is proposed in two-dimensional DOA estimation for coherent sources. The method inherit not only the good search capability and efficiency of quantum genetic algorithm, but also inherit the inhibition for impulsive noise of fractional lower order matrix. The results of simulation show the efficient of new method.