The Smooth Trajectory Estimator for LMB Filters
2023 12th International Conference on Control, Automation and Information Sciences (ICCAIS)(2024)
摘要
This paper proposes a smooth-trajectory estimator for the labelled
multi-Bernoulli (LMB) filter by exploiting the special structure of the
generalised labelled multi-Bernoulli (GLMB) filter. We devise a simple and
intuitive approach to store the best association map when approximating the
GLMB random finite set (RFS) to the LMB RFS. In particular, we construct a
smooth-trajectory estimator (i.e., an estimator over the entire trajectories of
labelled estimates) for the LMB filter based on the history of the best
association map and all of the measurements up to the current time.
Experimental results under two challenging scenarios demonstrate significant
tracking accuracy improvements with negligible additional computational time
compared to the conventional LMB filter. The source code is publicly available
at https://tinyurl.com/ste-lmb, aimed at promoting advancements in MOT
algorithms.
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关键词
Labelled multi-Bernoulli filter,estimator,smoothing,STE-LMB,RFS
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