Source tracking with a gradient-based eigenstructure algorithm

A new gradient-based eigenstructure algorithm to locate and track the azimuth and elevation angles of unknown sources in a 3-dimensional environment is proposed and investigated. Starting from initial estimates of the source locations from, say, a coarse search of the multiple signal classification (MUSIC) spectrum, the algorithm obtains better estimates and tracks sources in a repetitive cyclical manner. To refine a particular source location estimate: (1) a preprocessor is designed to remove the effects of the other sources; (2) the eigenvector for the largest eigenvalue of the covariance matrix after preprocessing is found and its difference from the ideal value is determined; and finally (3) a gradient calculation is used to obtain an estimate for the difference of the assumed source location from the actual position. The advantages of using the proposed technique rather than performing a thorough MUSIC search in the 2-dimensional spectrum are: (1) the implementation complexity can be reduced considerably, and (2) the algorithm is well suited to be employed for continuous adaptive filtering purposes in a tracking scenario