Joint estimation of amplitude, direction of arrival and range of near field sources using memetic computing

Abstract

In this paper, we propose a method based on evolutionary computations for joint estimation of amplitude, Direction of Arrival and range of near field sources. We use memetic computing in which the problem starts with a global optimizer and ends up with a local optimizer for fine tuning. For this, we use Genetic algorithm and Simulated annealing as a global optimizer while Interior Point Algorithm as a rapid local optimizer. We set up Mean Square Error as a fitness evaluation function which defines an error between actual and estimated signal. This fitness function is optimum and is derived from Maximum likelihood principle. It requires only single snapshot to converge and does not require any permutations to link it with the angles found in the previous snapshot as in some other methods. The efficiency and reliability of the proposed scheme is tested on the basis of Monte-Carlo simulations and its inclusive statistical analysis.