REAL-TIME IMPLEMENTATION OF PROPAGATOR BEARING ESTIMATION ALGORITHM BY USE OF A NEURAL NETWORK

This paper presents a neural network to implement the Marcos "propagator" bearing estimation algorithm. 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 propagator operator and the orthogonal projection operator on the noise subspace 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 cross-spectral matrix without an computations.