Real-time neural computation of the noise subspace for the MUSIC algorithm

A neural network approach to computing in real time the noise subspace for the MUSIC bearing estimation algorithm is proposed. The authors show analytically and by simulation results that the proposed neural network is guaranteed to provide the solution arbitrarily close to the accurate noise subspace during an elapsed time of only a few characteristic time constants of the circuit. The key features of this proposed computational approach are asynchronous parallel processing, continuous-time dynamics, and a high-speed computational compatibility.<>