Distributed Accumulated State Density (DASD) filtering is an effective strategy for optimal multi-sensor fusion. However, the fusion of Accumulated State Densitys (ASDs) with unequal window lengths, as occurs in typical multi-sensor multiobject applications, has not yet been covered in the literature. This paper aims to fill that gap by exploring various approaches, which range from ASD cutting to more sophisticated ASD adaptation and ASD zero-padding. In consequence, we arrive at the optimal ASD-Information Matrix Fusion or ASD-Tracklet Fusion, respectively. Both provide the optimal and full ASD result with full-rate transmission of standard (single state) densities only, thus reducing the communication load compared to a corresponding transmission of full ASDs. These methods perform particularly well in practical scenarios where the models are not perfectly adapted. All approaches are discussed theoretically and are thoroughly evaluated in simulation.

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.

The main challenge in random finite set-based multi-sensor multi-object tracking is the NP-hard association of the sensor measurements with the tracks. Using the Bayes parallel combination rule, we have recently proposed the Product Multi-sensor Generalized Labeled Multi-Bernoulli (PM-GLMB) filter, decomposing the multi-sensor update into simpler single-sensor problems with subsequent Bayes optimal fusion. This paper extends the filter to prior densities with Gaussian mixture spatial distributions, which is an inevitable prerequisite for the Product Multi-sensor Labeled Multi-Bernoulli (PM-LMB) filter we propose afterward. Finally, we evaluate the performance of both in a simulation study. In this context, we address the known performance problems of the PM-GLMB filter in crowded situations and show how the PM-LMB filter overcomes these.