Relating Random Vector and Random Finite Set Estimation in Navigation, Mapping, and Tracking

Leung, Keith Y. K.; Inostroza, Felipe; Adams, Martin

doi:10.1109/tsp.2017.2701330

Cited by 14 publications

(9 citation statements)

References 40 publications

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“…The last but not least, power fluctuation causes clutters to enter among leaders. On the one hand, random finite set (RFS) concept skips performing the data association (DA) algorithm before the state estimation phase, which is mandatory in random vector (RV) concept [11]. On the other hand, potential variety in the cardinality of a set can deal with alternative characteristics of extracted data.…”

Section: Justification Of a Random Finite Setmentioning

confidence: 99%

A novel motion-model-free UWB short-range positioning method

Navid

Ghavami

Rabiei

2019

SIViP

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In recent years, the number of location-based services is increasing and consequently, the researchers' attentions are captivated in designing accurate real-time positioning systems. Despite having a good performance in outdoor environment, global positioning system is not capable of estimating an object's position in an indoor environment precisely. In this paper, we present a novel tracking algorithm for indoor environment with a known floor plan. The object location is estimated by utilizing the information of the multipath components which are created by one physical and some virtual anchors. We will link this information to the floor plan by defining a channel model that has a combination of stochastic and deterministic traits. As we have used only one physical anchor in this paper, we would encounter several challenges such as lack of data association and existence of clutters amid real data. We dealt with these problems through random finite set methodology. Additionally, we will demonstrate that the proposed method is not restricted by the model of the motion and is capable to precisely track the trajectory. It will be shown that it provides a better accuracy, particularly in nonlinear trajectories, compared with two other relevant models which are adopting linear motion model.

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Section: Justification Of a Random Finite Setmentioning

confidence: 99%

A novel motion-model-free UWB short-range positioning method

Navid

Ghavami

Rabiei

2019

SIViP

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“…We note that there are also some other attempts at extending the conventional PF based on random set representation of the state and observation, which is different from that of Mahler’s formulation, such as [ 140 , 141 ]. Connection or re-derivation of traditional M2T association approaches based on a random finite set can be found in [ 142 , 143 ].…”

Section: Multitarget Pfmentioning

confidence: 99%

A Survey of Recent Advances in Particle Filters and Remaining Challenges for Multitarget Tracking

Wang

Sun

et al. 2017

Sensors

128

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We review some advances of the particle filtering (PF) algorithm that have been achieved in the last decade in the context of target tracking, with regard to either a single target or multiple targets in the presence of false or missing data. The first part of our review is on remarkable achievements that have been made for the single-target PF from several aspects including importance proposal, computing efficiency, particle degeneracy/impoverishment and constrained/multi-modal systems. The second part of our review is on analyzing the intractable challenges raised within the general multitarget (multi-sensor) tracking due to random target birth and termination, false alarm, misdetection, measurement-to-track (M2T) uncertainty and track uncertainty. The mainstream multitarget PF approaches consist of two main classes, one based on M2T association approaches and the other not such as the finite set statistics-based PF. In either case, significant challenges remain due to unknown tracking scenarios and integrated tracking management.

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“…False alarms, misdetections and data association uncertainty are the main challenges in multi-object tracking systems. The random finite set (RFS) [8], [9] is a popular multi-target estimation paradigm with applications in cell biology [10]- [12], traffic monitoring [13]- [16], field robotics [17]- [19], computer vision [11], [20]- [22], sonar [23], sensor network and distributed estimation [24]- [29], simultaneous localization and mapping [30]- [33], etc.…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensor Space Debris Tracking for Space Situational Awareness With Labeled Random Finite Sets

Wei

Nener

2019

IEEE Access

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As a result of the dependence worldwide on satellite technology, it is now necessary to use advanced multi-target tracking algorithms for space debris tracking systems to maintain custody of space objects around the earth. One principal challenge is the correct association of observations with objects. This paper presents a multi-sensor, space-debris tracking algorithm using δ-generalized labeled multi-Bernoulli (δ-GLMB) filtering. The algorithm provides a solution to the key challenges (e.g., detection uncertainty, data association uncertainty, and clutter) in multiple object tracking. An efficient implementation of the multi-sensor δ-GLMB filter is proposed. In order to avoid exhaustively computing all the terms, we propose to use the ranked assignment algorithm with an extended assignment matrix for multiple sensors to determine the most significant terms. A measurement-based birth model is used to identify the previously unknown space objects. Sensors can have the same or different observation volumes. The expectation-maximization (EM) algorithm is used to approximate densities across observation volumes. The performance is demonstrated using the simulation results.

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Relating Random Vector and Random Finite Set Estimation in Navigation, Mapping, and Tracking

Cited by 14 publications

References 40 publications

A novel motion-model-free UWB short-range positioning method

A novel motion-model-free UWB short-range positioning method

A Survey of Recent Advances in Particle Filters and Remaining Challenges for Multitarget Tracking

Multi-Sensor Space Debris Tracking for Space Situational Awareness With Labeled Random Finite Sets

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