IMPROVED SEQUENTIAL MUSIC

MUSIC (multiple signal classification) is one of the most frequently c onsidered methods for source location using sensor arrays. Among the l ocation methods based on one-dimensional search, MUSIC has excellent p erformance. in fact, no other one-dimensional method that may outperfo rm MUSIC (in large samples) was known to exist Our goal here is to int roduce such a method, called improved sequential MUSIC (IES-MUSIC), wh ich is shown to be strictly more accurate than MUSIC (in large samples ). First, a class of sequential MUSIC estimates is introduced, which d epend on a scalar-valued user parameter. MUSIC is shown to be a specia l case of estimate in that class, corresponding to a value of uro for the user parameter. Next, the optimal user parameter value, which mini mizes the asymptotic variance of the estimation errors, is derived IES -MUSIC is the method based on that optimal choice of the user paramete r. Simulation results which Lend support to the theoretical findings a re included.