A robust and efficient algorithm for source parameter estimation

A large number of array processing applications such as radar, sonar, etc. require the estimation of some parameters given the output of an array of sensors. Many high resolution methods for source parameter estimation are based on the eigen decomposition of the covariance matrix of the sensor output. The PASTd (projection approximation subspace tracking with deflation) algorithm [Yang, 1994] has been published for tracking both the signal subspace and its rank at a computational cost of order O(nr), where n is the number of sensors and r the number of sources to be detected. The present authors address the problem of tracking the physical parameters as direction, distance, etc. given the estimated signal subspace. All known parameter estimation methods as MUSIC, MinNorm or WSF are based on a different cost function which is minimized with respect to the desired parameters. Standard minimization methods as gradient or Newton´s method fail to converge to the global minimum if the starting value is not close enough to the desired solution [Viberg, 1991]. The present authors introduce a new cost function which has to be minimized with respect to the parameters and an algorithm of low computational cost which is able to find the global minimum, starting from any initial value in all the experiments