Direction of arrival tracking in impulsive noise using particle filtering with fractional lower order moment likelihood

Tracking the direction of arrival (DOA) of an acoustic source in an impulsive noise environment is a challenging problem due to the non-Gaussian characteristic of the noise process. In this paper, a particle filtering (PF) with fractional lower order moment (FLOM) likelihood model is developed to solve this problem. A constant velocity model is employed to model source dynamics and alpha-stable processes are used to model the impulsive noise environment. Since the second order statistics of alpha-stable processes do not exist, the FLOM matrix of the received array data is used to replace the covariance matrix to formulate a spatial spectra based pseudo likelihood. The likelihood is further exponentially weighted to enhance the weight of particles at high likelihood area and thus reduce the effect due to noise. The simulated experiments show that the proposed PF tracking algorithm significantly outperforms the existing PF as well as the Capon likelihood based PF under different impulsive noise environments.