Performance Enhancement of Capon’s DOA Algorithm Using Covariance Matrix Decomposition

Abstract

This paper deals with the problem of the direction of arrival (DOA) estimation for diverse systems of wireless communication using an antenna array. This study provides an improved version of Capon’s direction of arrival algorithm. In fact, the proposed version uses an upper-triangular matrix extracted from the covariance matrix instead of the entire covariance matrix. The simulation results demonstrate that our proposed scheme can significantly improve the accuracy of direction of arrival estimation with low computation complexity.

Positive Definiteness of the Sample Covariance Matrix of Multi-poisson Distribution

“…Covariance matrix is an important tool which is widely used in studying noise [1][2], direction-ofarrival [3][4][5], error distribution [6], allocation strategies [7], image analysis [8], power state estimation [9], local path planning [10], human activity recognition [11], geomagnetic jerk [12], the qualities of software and the sustainable innovation ability of enterprises [13][14][15], etc.. An important feature of the ML approach is that it has robust performance in noise environment by treating the covariance matrix of the additive Gaussian noise as a parameter [2]. In the image matching algorithm, The gradient magnitudes, direction, corrosion, expansion and information entropy and so forth, the feature of the target image can be reconciled with their covariance matrix to construct a new characteristic model [8].…”