Derivative MUSIC angle estimation

A planar array spatial spectrum estimator has been developed from an extension to a-MUSIC (1995), for discriminating between two closely spaced sources over a wide range of signal correlations. For two closely spaced and/or correlated signals, the second eigenvector in the signal subspace is not observable since the signal covariance matrix tends to be singular. Thus standard MUSIC methods only find one source which is a power weighted centroid of these signals. The δ-MUSIC spatial spectrum estimator derives an estimate of the second source eigenvector by applying a spatial derivative operator to the covariance matrix. A new signal subspace is formed by a projection operator which combines the first eigenvector and the second eigenvector estimate. The δ-MUSIC algorithm has been tested, modified and compared to the MUSIC algorithm using a point source simulation for both a linear and a planar array at various levels of correlation for two sources. The algorithm is also tested with simulated data from a Terrain Scattered Interference Source. The algorithm is found to be relatively insensitive to correlation and can separate targets to better than one-half the angular separation of MUSIC