REAL-TIME NEURAL COMPUTATION OF THE MAXIMUM-LIKELIHOOD CRITERION FOR BEARING ESTIMATION PROBLEMS

This paper presents a neural network to compute the projection operator which lies the major computational burden of the real-time computation of the maximum likelihood criterion for bearing estimation. We show both analytically and by simulations that this neural network is guaranteed to be stable and to provide the results arbitrarily close to the accurate projection operator within an elapsed time of only a few characteristic time constants of the network. The parameters such as the interconnection strengths and bias currents of this proposed network can be obtained from the given matrix without any computations.