Target Existence Based MHT

Abstract

In a cluttered environment, measurements originate not only from the objects being tracked, but also from spurious sources. The number of existing objects (targets) to be tracked is also unknown. The Multi-Hypotheses Tracking (MHT) filter is generally considered to be the "optimal" n-scan target tracking filter in such a multi-target cluttered environment. This paper introduces a new MHT filter based on the target existence. The probability that a target exists is estimated, and the estimate of the target kinematic state is subsequently conditioned on target existence. False track discrimination uses the probability of target existence as the track quality measure. This approach simplifies the structure of the algorithm and reduces complexity. A simplified version of the algorithm, which decouples new track hypotheses from the measurement-to-existing-track allocations is also presented. A simulation study shows the effectiveness of this approach in an environment of heavy and non-uniform clutter, with multiple maneuvering targets with crossing trajectories.