Multirate interacting multiple model filtering for target tracking using multirate models

Abstract

A multirate interacting multiple model (MRIMM) tracking algorithm has been developed in this paper. The algorithm is based on a reformulation of the interacting multiple model (IMM) filter under the assumption that each model operates at an update rate proportional to the model's assumed dynamics. A set of multirate models is derived based on the geometrical interpretation of a discrete wavelet transform. A wavelet transform is used to generate equivalent multirate measurements, which exhibit the additional property of lower equivalent measurement noise for low-rate data. Using this filtering approach, the MRIMM algorithm significantly outperforms a full-rate IMM filter when no maneuvers occur and performs comparably with the IMM filter when maneuvers occur, with a certain amount of computational savings. This approach also has the advantages of improved sensitivity for maneuver detection.