Decentralized Gaussian Filters for Cooperative Self-Localization and Multi-Target Tracking

Sharma, P.; Saucan, Augustin-Alexandru; Bucci, Donald J.; Varshney, Pramod K.

doi:10.1109/tsp.2019.2946017

Cited by 49 publications

(32 citation statements)

References 36 publications

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“…The proposed solution considers the localization of cooperative agents and the multi-target tracking as two integrated tasks. Compared to state-of-the-art solutions [15][16][17][18][19], the developed methodology integrates the uncertainties of agents (coming from the need of localizing them) with the tracking of unknown and arbitrary number of targets, and fruitfully exploits target localization as a mean to improve agent self-localization. The idea can be conceptualized as a dual layer mutually-connected graph (see Fig.…”

Section: Cooperative Localization: Methodologymentioning

confidence: 99%

Sensor-Assisted Cooperative Localization and Communication in Multi-agent Networks

Brambilla

2022

Special Topics in Information Technology

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This brief highlights research advances on cooperative techniques for localization and communication. These two macro trends are investigated in the general context of mobile multi-agent networks for situational awareness applications, where time-varying agents of unknown locations are asked to fulfill positioning and information sharing tasks. Cooperative localization is conceived for both active and passive agents, i.e., targets to be detected and localized, and it is analyzed in vehicular and maritime environments. Communication is investigated for vehicular scenarios, where vehicles are requested to share massive data in the perspective development of connected and automated mobility systems. Both research areas rely on the integration of heterogeneous sensors and communication. Specifically, it is studied how to improve localization by exploring communication techniques as well as how to enhance communication performances by extracting information from perception sensors. The dynamic environment of multi-agent systems calls for robust, flexible and adaptive techniques, capable of profitably fuse different types of information, and the outcomes of these researches show how a statistical approach based on cooperation guarantees higher resilience, reliability and confidence.

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Section: Cooperative Localization: Methodologymentioning

confidence: 99%

Sensor-Assisted Cooperative Localization and Communication in Multi-agent Networks

Brambilla

2022

Special Topics in Information Technology

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“…In cases where positional states follow a linear evolution and both SFI and SCI are Gaussian functions, the inference can be performed in a closed form as in Kalman filters [11]. Otherwise, its implementation emploies approximations which account for a trade-off between complexity and accuracy [12]. Compared to existing works which rely on predefined measurement models, such as those in the field of multi-sensor multi-target tracking [13], SI-based approaches do not require specific measurement models.…”

Section: Soft Information For Location Awarenessmentioning

confidence: 99%

Location Awareness in Beyond 5G Networks

et al. 2021

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Location awareness is essential for enabling contextual services and for improving network management in 5th generation (5G) and beyond 5G (B5G) networks. This paper provides an overview of the expanding opportunities offered by location awareness in wireless networks, discusses soft information (SI)-based approaches for improved location awareness, and presents case studies in conformity to the 3rd Generation Partnership Project (3GPP) standardization by the European Telecommunications Standards Institute (ETSI). Results show that SI-based approaches can provide a new level of location awareness in 5G and B5G networks.

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“…We propose a SPAbased technique that extends the state-of-the-art methods by combining cooperative self-localization and multitarget tracking in a unified framework such that agents are capable of jointly localizing themselves and, at the same time, detecting and tracking an unknown, unlimited, and time-varying number of targets in presence of clutter, miss detection and association uncertainty. A fully distributed approach based on consensus strategies [50], [54]- [58] can be adopted and customized for the proposed scheme; this study is not included here and left to future works. We focus the attention on the holistic approach for cooperative self-localization and multitarget tracking, and on the real world experimentation.…”

Section: B Contributions and Paper Organizationmentioning

confidence: 99%

“…However, not all of them handle typical multitarget tracking challenges like the presence of clutter generated measurements (i.e., false alarms), missed detections, and measurement origin uncertainty [52], i.e., the problem of unknown association between targets and measurements. Focusing on multitarget tracking algorithms with mobile sensors, the cited works are affected by the following limitations: in [49] sensors do not localize themselves cooperatively; in [50] the maximum number of targets that can be tracked simultaneously is limited and needs to be set a priori; in [16] and [51] the number of targets is time-invariant and known, and, in addition, in [16] neither false alarms nor missed detections are considered, and in [51] the association between targets and measurements is assumed known. Moreover, the solutions proposed in these works are not suitable for a full multistatic network configuration, and do not consider the case of signal reflections from agents, thus easing the data association problem.…”

Section: Introduction a Background And Motivationmentioning

confidence: 99%

Cooperative Localization and Multitarget Tracking in Agent Networks with the Sum-Product Algorithm

Brambilla,

Gaglione,

Soldi

et al. 2021

Preprint

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This paper addresses the problem of multitarget tracking using a network of sensing agents with unknown positions. Agents have to both localize themselves in the sensor network and, at the same time, perform multitarget tracking in the presence of clutter and target miss detection. These two problems are jointly resolved in a holistic approach where graph theory is used to describe the statistical relationships among agent states, target states, and observations. A scalable message passing scheme, based on the sum-product algorithm, enables to efficiently approximate the marginal posterior distributions of both agent and target states. The proposed solution is general enough to accommodate a full multistatic network configuration, with multiple transmitters and receivers. Numerical simulations show superior performance of the proposed joint approach with respect to the case in which cooperative self-localization and multitarget tracking are performed separately, as the former manages to extract valuable information from targets. Lastly, data acquired in 2018 by the NATO Science and Technology (STO) Centre for Maritime Research and Experimentation (CMRE) through a network of autonomous underwater vehicles demonstrates the effectiveness of the approach in practical applications.

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Decentralized Gaussian Filters for Cooperative Self-Localization and Multi-Target Tracking

Cited by 49 publications

References 36 publications

Sensor-Assisted Cooperative Localization and Communication in Multi-agent Networks

Sensor-Assisted Cooperative Localization and Communication in Multi-agent Networks

Location Awareness in Beyond 5G Networks

Cooperative Localization and Multitarget Tracking in Agent Networks with the Sum-Product Algorithm

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