Extended Target Frequency Response Estimation Using Infinite Hmm in Cognitive Radars
Ahmed A. Abouelfadl, Ioannis Psaromiligkos, Benoı̂t Champagne · 2019
A cognitive radar adapts its waveform to match the extended target's frequency response (TFR) for optimized detection performance. In practice, the TFR is unknown and is usually estimated using the Kalman filter assuming a linear Gaussian model. However, this assumption is not always fulfilled and other filters as the particle filter should be used. In all cases, existing approaches require the complete knowledge of the statistical distributions of both the TFR and interference. In this paper, we present a novel formulation of the TFR estimation problem that allows us to use the infinite hidden Markov model (iHMM) to estimate and track the TFR without such prior knowledge. Monte Carlo simulations considering Gaussian and non-Gaussian distributions for TFR and interference as well as jamming effects show that the proposed iHMM-based method ameliorates the estimation accuracy compared to the conventional Bayesian filtering techniques.