  The problem is target motion analysis (TMA), where the objective is to
estimate the state of a moving target from noise corrupted bearings-only
measurements. The focus is on recursive TMA, traditionally solved using the
Bayesian filters (e.g. the extended or unscented Kalman filters, particle
filters). The TMA is a difficult problem and may cause the algorithms to
diverge, especially when the measurement noise model is imperfect or
mismatched. As a robust alternative to the Bayesian filters for TMA, we propose
the recently introduced possibility filter. This filter is implemented in the
sequential Monte Carlo framework, and referred to as the possibility particle
filter. The paper demonstrates its superior performance against the standard
particle filter in the presence of a model mismatch, and equal performance in
the case of the exact model match.
