Signal estimation in Bayesian field tracking

Bayesian field tracking, in which a posterior target distribution is developed over the entire position-velocity state space, is a track-before-detect approach with a demonstrated capability to track at SNR levels below those for which the usual Kalman-based tracker is functional. Development of the Bayesian posterior probabilities is recursive and is driven by likelihood fields evaluated from successive measurement observations. Previous Bayesian field tracker applications have constructed likelihood fields assuming a prior signal amplitude distribution that has remained unchanged throughout processing. In this paper we combine a I-D Kalman filter with the diffusive projection of the Bayesian field tracker to estimate an amplitude distribution at each point of the state-space field. Results show that this approach can provide likelihood growth approaching that for a known signal amplitude.