Recursive 'ML' bearing estimation: initialization and sources number update
P. Larzabal, H. Clergeot · 1992
Maximum-likelihood (ML) and approximate ML may be considered as the upper state of the art in high-resolution methods, but they suffer from initialization of the sources' number and position. Starting from a crude initialization with a low-resolution method, the authors propose a time recursive method for simultaneous update of the sources' number and location. For the current estimate of the sources' number the algorithm computes the best ML position estimate over the past observations. The corresponding signal is subtracted from the observations, and the residue is tested for the noise-only hypothesis. If the test fails, the sources' number is incremented, a new initialization is provided, and ML estimation proceeds. Emphasis is on the stationary case.>