An Efficient Implementation of the Fast Product Multi-Sensor Labeled Multi-Bernoulli Filter

In Random Finite Set based multi-sensor multi-object tracking, the NP-hard measurement-to-track assignment problem is a key challenge. One approach to address this challenge involves executing computationally simpler single-sensor updates based on a common prediction, followed by the fusion of these updates. This strategy is used by the already proposed Fast Product Multi-Sensor Labeled Multi-Bernoulli filter, which still poses computational challenges in its existing formulation. This paper introduces an efficient implementation of the Fast Product Multi-Sensor Labeled Multi-Bernoulli filter by improving the efficiency of the individual single-sensor updates and the fusion of the resulting single-sensor posterior densities. Two different approaches are presented for each part, including the new GeneralizedKBestSelection algorithm, which solves a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> shortest path problem on highly structured graphs. Our approach is evaluated on simulations, and the results are compared with an Iterated Corrector implementation of the Labeled Multi-Bernoulli filter using comparable simplifications.