Parallel Detection Fusion for Multisensor Tracking of a Maneuvering Target in Clutter Using IMMPDA Filtering
Abstract
We present a (suboptimal) filtering algorithm for tracking a highly maneuvering target in a cluttered environment using multiple sensors. The filtering algorithm is developed by applying the basic Interacting Multiple Model (IMM) approach and the Probabilistic Data Association (PDA) technique to a two sensor (radar and infrared, for instance) problem for state estimation for the target. A detection fusion approach is followed where the raw sensor measurements are passed to a fusion node and fed directly to the target tracker. A multisensor probabilistic data association filter is developed for parallel sensor processing for target tracking under clutter. A past approach using parallel sensor processing has ignored certain data association probabilities leading to an erroneous derivation. Another existing approach applies only to non-maneuvering targets. 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 sequential sensor processing, the proposed algorithm achieves significant improvement in the accuracy of track estimation.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2003
- Accession Number
- ADA417737
Entities
People
- Jitendra K. Tugnait
- Soonho Jeong
Organizations
- Auburn University