A particle filter for track-before-detect of a target with unknown amplitude viewed against a structured scene

Track-before-detect methods operate directly upon raw sensor signals without a separate, explicit detection stage. An efficient implementation of a Bayesian track-before-detect particle filter is described for tracking of a single target in a sequence of images. 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. Spatial differentiation of the pixel array data allows objects to be tracked when viewed against a general scene with additive noise. Simulated results illustrate that a dim point target of unknown amplitude, which has become spatially blurred, may be tracked through a sequence of structured images. Detection sensitivity is established using simulated results.