In this study, we provide an overview of recent advances in multisensor multitarget tracking
based on the random finite set (RFS) approach. The fusion that plays a fundamental role in …

We propose an algorithm for tracking an unknown number of targets based on
measurements provided by multiple sensors. Our algorithm achieves low computational …

In this chapter, we provide an overview of cutting-edge approaches and remaining
challenges in multisensor multitarget information fusion based on the random finite set …

In this paper, we derive a multisensor multi-Bernoulli (MS-MeMBer) filter for multitarget
tracking. Measurements from multiple sensors are employed by the proposed filter to update …

Scalable and decentralized algorithms for Cooperative Self-localization (CS) of agents, and
Multi-Target Tracking (MTT) are important in many applications. In this work, we address the …

The single-sensor probability hypothesis density (PHD) and cardinalized probability
hypothesis density (CPHD) filters have been developed in the literature using the random …

Recent progress in multi-object filtering has led to algorithms that compute the first-order
moment of multi-object distributions based on sensor measurements. The number of targets …

Tracking an unknown number of objects is challenging, and often requires looking beyond
classical statistical tools. When many sensors are available the estimation accuracy can …

A new multiple extended target tracking algorithm using the probability hypothesis density
(PHD) filter is proposed in our study, to solve problems on tracking performance degradation …

The number of fatal accidents involving pedestrians and bikers at urban intersections is still
increasing. Therefore, an intersection-based perception system provides a dynamic model …