Related papers: Multi-Bernoulli Sensor-Control via Minimization of…
The recently developed labeled multi-Bernoulli (LMB) filter uses better approximations in its update step, compared to the unlabeled multi-Bernoulli filters, and more importantly, it provides us with not only the estimates for the number of…
A novel sensor control solution is presented, formulated within a Multi-Bernoulli-based multi-target tracking framework. The proposed method is especially designed for the general multi-target tracking case, where no prior knowledge of the…
In this paper we derive a multi-sensor multi-Bernoulli (MS-MeMBer) filter for multi-target tracking. Measurements from multiple sensors are employed by the proposed filter to update a set of tracks modeled as a multi-Bernoulli random finite…
Sensor management in multi-object stochastic systems is a theoretically and computationally challenging problem. This paper presents a novel approach to the multi-target multi-sensor control problem within the partially observed Markov…
In this paper, we propose two efficient, approximate formulations of the multi-sensor labelled multi-Bernoulli (LMB) filter, which both allow the sensors' measurement updates to be computed in parallel. Our first filter is based on the…
In multi-object stochastic systems, the issue of sensor management is a theoretically and computationally challenging problem. In this paper, we present a novel random finite set (RFS) approach to the multi-target sensor management problem…
Information-driven control can be used to develop intelligent sensors that can optimize their measurement value based on environmental feedback. In object tracking applications, sensor actions are chosen based on the expected reduction in…
In multi-object tracking applications, model parameter tuning is a prerequisite for reliable performance. In particular, it is difficult to know statistics of false measurements due to various sensing conditions and changes in the field of…
In this paper, we propose a non-myopic sensor management algorithm for multi-target tracking, with multiple sensors operating in the same surveillance area. The algorithm is based on multi-Bernoulli filtering and selects the actions that…
Autonomous vehicles need precise knowledge on dynamic objects in their surroundings. Especially in urban areas with many objects and possible occlusions, an infrastructure system based on a multi-sensor setup can provide the required…
With the increasing complexity of multiple target tracking scenes, a single sensor may not be able to effectively monitor a large number of targets. Therefore, it is imperative to extend the single-sensor technique to Multi-Sensor…
A robust (deterministic) filtering approach to the problem of optimal sensor selection is considered herein. For a given system with several sensors, at each time step the output of one of the sensors must be chosen in order to obtain the…
The sensor whose output is a function of the sum of contributions from targets present in the surveillance area is called superpositional sensor. In this letter, target clustering based multi-Bernoulli filter for superpositional sensors is…
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…
In this paper, we propose an algorithm for non-myopic sensor management for Bernoulli filtering, i.e., when there may be at most one target present in the scene. The algorithm is based on selecting the action that solves a Bellman-type…
A decentralized Poisson multi-Bernoulli filter is proposed to track multiple vehicles using multiple high-resolution sensors. Independent filters estimate the vehicles' presence, state, and shape using a Gaussian process extent model; a…
Technological advancements in miniaturization and wireless communications are yielding more affordable and versatile sensors and, in turn, more applications in which a network of sensors can be actively managed to best support overall…
This paper proposes an efficient implementation of the multi-sensor generalized labeled multi-Bernoulli (GLMB) filter. The solution exploits the GLMB joint prediction and update together with a new technique for truncating the GLMB…
We propose a method for tracking an unknown number of targets based on measurements provided by multiple sensors. Our method achieves low computational complexity and excellent scalability by running belief propagation on a suitably devised…
In a typical multitarget tracking (MTT) scenario, the sensor state is either assumed known, or tracking is performed in the sensor's (relative) coordinate frame. This assumption does not hold when the sensor, e.g., an automotive radar, is…