Multisensor Tracking of a Maneuvering Target in Clutter with Proposed Parallel Updating Approach

Abstract

To solve the problem of measurement original uncertainty, we present a proposed parallel updating approach for tracking a maneuvering target in cluttered environment using multiple sensors. A parallel updating method is followed where the raw sensor measurements are passed to a central processor and fed directly to the target tracker. A past approach using parallel sensor processing has ignored certain data association probabilities. Simulation results show that compared with an existing IMMPDAF algorithm with parallel sensor approach, the IMMPDAF algorithm with proposed parallel updating approach solves the problem of measurements' origins and achieves significant improvement in the accuracy of track estimation.