A particle filter for track-before-detect of a target with unknown amplitude

A Bayesian track-before-detect particle filter is proposed. The filter provides a sample based approximation to the distribution of the target state directly from pixel array data. The filter also provides a measure of the probability that the target is present. Results are presented for tracking a dim point target of unknown amplitude that has become blurred via a Gaussian point spread function.