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The problem is target motion analysis (TMA), where the objective is toestimate the state of a moving target from noise corrupted bearings-onlymeasurements. The focus is on recursive TMA, traditionally solved using theBayesian filters (e.g. the extended or unscented Kalman filters, particlefilters). The TMA is a difficult problem and may cause the algorithms todiverge, especially when the measurement noise model is imperfect ormismatched. As a robust alternative to the Bayesian filters for TMA, we proposethe recently introduced possibility filter. This filter is implemented in thesequential Monte Carlo framework, and referred to as the possibility particlefilter. The paper demonstrates its superior performance against the standardparticle filter in the presence of a model mismatch, and equal performance inthe case of the exact model match.

Robust TMA using the possibility particle filter