Estimation of multimodal posterior distributions of chirp parameters with population Monte Carlo sampling

Chirp signals are usually encountered in target tracking problems including radar and sonar systems. The multimodality characterizing the distribution of the chirp signal parameters makes their estimation very challenging. In this paper we apply marginalized population Monte Carlo (MPMC) sampling to the problem of parameter estimation of chirp signals in noise. MPMC reduces the dimension of the vector of unknowns by marginalizing the complex amplitudes, which are conditionally linear on the chirp rates and frequencies. A Gibbs sampling scheme is combined with the MPMC method to further improve the performance. Computer simulations illustrate the validity of the proposed approach.