Multisensor Tracking of a Maneuvering Target in Clutter with Asychronous Measurements using IMMPDA Filtering and Parallel Detection Fusion

Abstract

We present a (suboptimal) filtering algorithm for tracking a highly maneuvering target in a cluttered environment using multiple sensors dealing with possibly asynchronous (time delayed) measurements. The filtering algorithm is developed by applying the basic Interacting Multiple Model (IMM) approach, the Probabilistic Data Association (PDA) technique, and asynchronous measurement updating for state-augmented system estimation for the target. A state augmented approach is developed to estimate the time delay between local and remote sensors. A multisensor probabiHstic data association filter is developed for parallel sensor processing for target tracking under clutter. The algorithm is illustrated via a highly maneuvering target tracking simulation example where two sensors, a radar and an infrared sensor, are used. Compared with an existing IMMPDA filtering algorithm with the assumption of synchronous (no delay) measurements sensor processing, the proposed algorithm achieves considerable improvement (especially in the case of larger delays) in the accuracy of track estimation.

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

“…The class of the problem of target a maneuvering target in clutter has received considerable attention, and a lot of tracking filters have been developed [1][2][3][4].While tracking a target in clutter, the measurements at the sensors may not all have originated from the target of interest. One has to account for measurements uncertainty origin, i.e., how to associate the data available with a target in clutter (a false measurement).In the Bayesian framework, PDAF is usually used to solve the problem of data association and target tracking [5][6][7].…”