A new particle filter for nonlinear tracking problems

A new particle filter is presented for nonlinear tracking problems. In practice, maneuvering target-tracking systems are usually nonlinear and incompletely observed, and the main difficulty of maneuvering target-tracking problem lies in the fact that the maneuverability at every step is of high uncertainties. Here a new smoothing particle filter algorithm is proposed, which combines the particle filter to tackle the non-linear and non-Gaussian peculiarities of the problem, together with smoothing of the PDF of system modes and thus settles the estimate problem of the target maneuverability. The simulation comparison with the auxiliary particle filters shows that the approach has superiority and yields performance improvements in solving nonlinear tracking problems.