The Adaptive Labeled Multi-Bernoulli Filter
For researchers in multi-target tracking, this filter offers a trade-off between accuracy and efficiency, but the improvement is incremental.
The paper proposes the Adaptive Labeled Multi-Bernoulli filter, which combines strengths of existing filters to improve target tracking precision in critical situations while reducing computational complexity in noncritical ones.
This paper proposes a new multi-Bernoulli filter called the Adaptive Labeled Multi-Bernoulli filter. It combines the relative strengths of the known Delta-Generalized Labeled Multi-Bernoulli and the Labeled Multi-Bernoulli filter. The proposed filter provides a more precise target tracking in critical situations, where the Labeled Multi-Bernoulli filter looses information through the approximation error in the update step. In noncritical situations it inherits the advantage of the Labeled Multi-Bernoulli filter to reduce the computational complexity by using the LMB approximation.