Robust signal modeling through nonlinear least squares

A nonlinear least-squares (LS) method is developed for modeling empirically obtained data in array signal processing. The new method is robust with respect to modeling errors in the noise distribution. Robustness is achieved by introducing a nonlinear function which weights the squared error term in the LS criterion. Weighting functions for various observation noise scenarios are determined by using maximum likelihood estimation theory. The computational complexity of the new method is comparable to the standard least-squares estimation procedures. Simulation examples of direction-of-arrival (DOA) estimation are presented