ML-PDA estimation of RCS in the presence of false measurements

A maximum likelihood (ML) approach is presented for estimating the mean of radar cross section (RCS) of a Swerling target in the presence of false measurements and its numerical solution methods are discussed. The ML approaches are based on the maximum likelihood probabilistic data association (ML-PDA) formulation. The numerical solution methods are evaluated through Monte Carlo simulations in terms of estimation accuracy and computational time.