Fixed-Lag Smoothing for Bayes Optimal Knowledge Exploitation in Target Tracking

In this work, we are interested in the improvements attainable when multiscan processing of external knowledge is performed over a moving time window. We propose a novel algorithm that enforces the state constraints by using a Fixed-Lag Smoothing procedure within the prediction step of the Bayesian...

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Bibliographic Details
Published in:IEEE transactions on signal processing 2014-06, Vol.62 (12), p.3143-3152
Main Authors: Papi, Francesco, Bocquel, Melanie, Podt, Martin, Boers, Yvo
Format: Article
Language:English
Subjects:
Applied sciences
Approximation methods
Bayes methods
constrained filtering
Detection, estimation, filtering, equalization, prediction
differential entropy
Entropy
Exact sciences and technology
External knowledge
fixed-lag smoothing
Information, signal and communications theory
Radar tracking
sequential Monte Carlo
Signal and communications theory
Signal, noise
Smoothing methods
Target tracking
Telecommunications and information theory
Uncertainty
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Summary:In this work, we are interested in the improvements attainable when multiscan processing of external knowledge is performed over a moving time window. We propose a novel algorithm that enforces the state constraints by using a Fixed-Lag Smoothing procedure within the prediction step of the Bayesian recursion. For proving the improvements, we utilize differential entropy as a measure of uncertainty and show that the approach guarantees a lower or equal posterior differential entropy than classical single-step constrained filtering. Simulation results using examples for single-target tracking are presented to verify that a Sequential Monte Carlo implementation of the proposed algorithm guarantees an improved tracking accuracy.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2014.2321731