Network Localization and Navigation With Scalable Inference and Efficient Operation

Teague, Bryan; Liu, Zhenyu; Meyer, Florian; Conti, Andrea; Win, Moe Z.

doi:10.1109/tmc.2020.3035511

Cited by 20 publications

(8 citation statements)

References 73 publications

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“…In particular, by performing local operations ("messages") on the factor graph, accurate approximations ("beliefs") of the marginal posterior pdfs of unknown states [14] are computed. SPA-based methods are versatile and have been successfully applied to a variety of applications, including cooperative localization [15]- [18], simultaneous localization and mapping (SLAM) [19]- [21], and focalization for underwater localization [22].…”

Section: A State-of-the-artmentioning

confidence: 99%

Multisensor Multiobject Tracking With Improved Sampling Efficiency

Zhang,

Meyer

2024

IEEE Trans. Signal Process.

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Passive monitoring of acoustic or radio sources has important applications in modern convenience, public safety, and surveillance. A key task in passive monitoring is multiobject tracking (MOT). This paper presents a Bayesian method for multisensor MOT for challenging tracking problems where the object states are high-dimensional, and the measurements follow a nonlinear model. Our method is developed in the framework of factor graphs and the sum-product algorithm (SPA) and implemented using random samples or "particles". The multimodal probability density functions (pdfs) provided by the SPA are effectively represented by a Gaussian mixture model (GMM). To perform the operations of the SPA with improved sample efficiency, we make use of particle flow (PFL). Here, particles are migrated towards regions of high likelihood based on the solution of a partial differential equation. This makes it possible to obtain good object detection and tracking performance even in challenging multisensor MOT scenarios with single sensor measurements that have a lower dimension than the object positions. We perform a numerical evaluation in a passive acoustic monitoring scenario where multiple sources are tracked in 3-D from 1-D time-difference-of-arrival (TDOA) measurements provided by pairs of hydrophones. Our numerical results demonstrate favorable detection and estimation accuracy compared to state-of-the-art reference techniques.

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Section: A State-of-the-artmentioning

confidence: 99%

Multisensor Multiobject Tracking With Improved Sampling Efficiency

Zhang,

Meyer

2024

IEEE Trans. Signal Process.

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“…In [25], the authors propose a framework designed for scalable indoor localization and implement it using UWB radios. The work in [25] is focused on the software, which enables cooperation between both fixed and mobile nodes in order to achieve seamless localization. In comparison, we focus on the specific TDOA localization algorithm, which enables scalable and accurate localization.…”

Section: A Scalable Uwb Localizationmentioning

confidence: 99%

FlexTDOA: Robust and Scalable Time-Difference of Arrival Localization Using Ultra-Wideband Devices

et al. 2023

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The authors would like to acknowledge the support of NXP Romania towards supporting the PhD studies of George-Cristian Pȃtru, as well as providing valuable insights regarding trends for IoT solutions. The authors gratefully acknowledge funding from European Union's Horizon 2020 Research and Innovation programme under the Marie Sklodowska Curie grant agreement No. 813278 (A-WEAR: A network for dynamic wearable applications with privacy constraints). The results presented in this article were obtained with the support of the Ministry of European Investments and Projects through the

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“…where each argument λ ( ) comprises certain parameter vectors λ d , and each λ d can appear in several λ ( ) . The factorization (28) can be represented by a factor graph, which is constructed as follows: each parameter variable λ d is represented by a variable node; each factor κ (•) is represented by a factor node; and variable node "λ d " and factor node "κ " are adjacent, i.e., connected by an edge, if λ d is an argument of κ (•).…”

Section: A Sum-product Algorithm: Overview and Notationmentioning

confidence: 99%

“…The SPA algorithm aims at computing the marginal posterior pdfs f (λ d |π) in an efficient way, and is based on the factor graph representing the factorization of f (λ|π) in (28). For each node in the factor graph, certain messages are calculated, each of which is then passed to one of the adjacent nodes.…”

Section: A Sum-product Algorithm: Overview and Notationmentioning

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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Network Localization and Navigation With Scalable Inference and Efficient Operation

Cited by 20 publications

References 73 publications

Multisensor Multiobject Tracking With Improved Sampling Efficiency

Multisensor Multiobject Tracking With Improved Sampling Efficiency

FlexTDOA: Robust and Scalable Time-Difference of Arrival Localization Using Ultra-Wideband Devices

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

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