The Fast Product Multi-Sensor Labeled Multi-Bernoulli Filter

EasyChair Preprint 10346

Abstract

The multi-sensor Labeled Multi-Bernoulli filter has the challenge of relying on the NP-hard multi-sensor update of the Generalized Labeled Multi-Bernoulli filter. This paper proposes the Fast Product Multi-Sensor Labeled Multi-Bernoulli filter, which is a filter for multi-sensor systems that solves this task by performing computationally simpler single-sensor Labeled Multi-Bernoulli filter updates based on a common prediction for each sensor. These single-sensor updates are then fused using a novel and efficient fusion strategy. Furthermore, the proposed filter is based on the Bayes parallel combination rule and can be seen as an efficient approximation of the multi-sensor Labeled Multi-Bernoulli filter. It enables full parallelization of the update step and benefits from sensor order independence compared to Iterated Corrector implementations. As a result, the robustness is increased, which is important for safety reasons, e.g., in autonomous driving. Our approach is evaluated on simulations, and the results are compared to an Iterated Corrector implementation of the Labeled Multi-Bernoulli filter.

Keyphrases: filtering, labeled random finite sets, multi-sensor multi-object tracking, state estimation

@booklet{EasyChair:10346,
  author    = {Charlotte Hermann and Martin Herrmann and Thomas Griebel and Michael Buchholz and Klaus Dietmayer},
  title     = {The Fast Product Multi-Sensor Labeled Multi-Bernoulli Filter},
  howpublished = {EasyChair Preprint 10346},
  year      = {EasyChair, 2023}}