Bearing estimation using neural optimisation methods

The bearing estimation problem is concerned with determining the directions of sources radiating an array of sensors, in the presence of additive noise. The authors have mapped the bearing estimation problem onto the Lyapunov energy function of the Hopfield model neural network. However, Hopfield model implements a gradient descent algorithm, and in common with all such algorithms, it is liable to find a local minima rather than the desired global minimum. To overcome this problem three modification, gain annealing, iterated descent and stochastic networks have been proposed. The authors outline and simulate the modifications to the neural algorithm and results are presented to show their convergence properties in the context of the bearing estimation problem