Sigma Point Belief Propagation

Meyer, Florian; Hlinka, Ondrej; Hlawatsch, Franz

doi:10.1109/lsp.2013.2290192

Cited by 81 publications

(76 citation statements)

References 17 publications

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“…However, this approach implicitly assumes a Gaussian ranging error distribution and thus is less accurate in typical indoor applications where the ranging errors are not Gaussian distributed. Similar observations can be made about [17], [21], which are also based on Gaussian ranging error models. In contrast, the proposed approach is more general and more suitable for indoor applications.…”

Section: B Computation Of Messagessupporting

confidence: 60%

“…The mobile nodes were assumed to have obtained the multihop distances to anchors using the DV-distance algorithm. • SPBP: sigma point belief propagation of [21] based on Gaussian ranging error model 3 in Table I. A similar approach as in [21] was employed to choose the prior distribution of each node's position, with the initial position variance set to 10.…”

Section: A Locating Static Nodesmentioning

confidence: 99%

“…An efficient BP algorithm (H-SPAWN) has been presented in [17] for systems with Gaussian ranging errors, which uses multivariate Gaussian distributions to approximate position uncertainties and computes peer-to-peer messages analytically. An extension of the sigma point filter is proposed in [21] for efficient belief propagation (SPBP). However, H-SPAWN, SPBP, and the "linear approximation" approach are based on Gaussian ranging error models.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

New Efficient Indoor Cooperative Localization Algorithm With Empirical Ranging Error Model

Li¹,

Hedley²,

Collings³

2015

IEEE J. Select. Areas Commun.

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Cooperative localization can improve both the availability and accuracy of positioning systems, and distributed belief propagation is a promising enabling technology. Difficulties with belief propagation lie in achieving high accuracy without causing high communication overhead and computational complexity. This limits its application in practical systems with mobile nodes that have limited battery size and processing capabilities. In this paper, we propose an efficient cooperative localization algorithm that can be applied to a real indoor localization system with a non-Gaussian ranging error distribution. We first propose an asymmetric double exponential ranging error model based on empirical ranging data. An efficient cooperative localization algorithm based on distributed belief propagation is then proposed. The communication and computational cost is reduced by passing approximate beliefs represented by Gaussian distributions between neighbours and by using an analytical approximation to compute peer-to-peer messages. An extension of the proposed algorithm is also proposed for tracking dynamic nodes. The proposed algorithms are validated on an indoor localization system deployed with 28 nodes covering 8000 m 2 , and are shown to outperform existing algorithms. In particular, the fraction of nodes located to one-meter accuracy is doubled using the proposed ranging error model and localization algorithm.Index Terms-Cooperative localization, belief propagation, indoor positioning, ranging error model.

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Section: B Computation Of Messagessupporting

confidence: 60%

Section: A Locating Static Nodesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

New Efficient Indoor Cooperative Localization Algorithm With Empirical Ranging Error Model

Li¹,

Hedley²,

Collings³

2015

IEEE J. Select. Areas Commun.

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“…In general, Bayesian belief propagation for cooperative localization suffers from the complexity arising from exchanging messages. Authors in [55] showed a sigma point belief propagation by which a low-complexity approximation can be achieved.…”

Section: G Localizationmentioning

confidence: 99%

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

Laoudias¹,

Moreira

Kim

et al. 2018

IEEE Commun. Surv. Tutorials

369

171

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Abstract-Location information for events, assets, and individuals, mostly focusing on two dimensions so far, has triggered a multitude of applications across different verticals, such as consumer, networking, industrial, health care, public safety, and emergency response use cases. To fully exploit the potential of location awareness and enable new advanced location-based services, localization algorithms need to be combined with complementary technologies including accurate height estimation, i.e., three dimensional location, reliable user mobility classification, and efficient indoor mapping solutions. This survey provides a comprehensive review of such enabling technologies. In particular, we present cellular localization systems including recent results on 5G localization, and solutions based on Wireless Local Area Networks (WLAN), highlighting those that are capable of computing 3D location in multi-floor indoor environments. We overview range-free localization schemes, which have been traditionally explored in Wireless Sensor Networks (WSN) and are nowadays gaining attention for several envisioned Internet of Things (IoT) applications. We also present user mobility estimation techniques, particularly those applicable in cellular networks, that can improve localization and tracking accuracy. Regarding the mapping of physical space inside buildings for aiding tracking and navigation applications, we study recent advances and focus on smartphone-based indoor Simultaneous Localization and Mapping (SLAM) approaches.The survey concludes with service availability and system scalability considerations, as well as security and privacy concerns in location architectures, discusses the technology roadmap, and identifies future research directions.

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“…However, the parametric models must be specially tailored for different localization scenarios [4,5]. In sigma point belief propagation (SPBP), the belief propagation (BP) or sum-product algorithm (SPA) is reformulated in a higher dimensional space so that the belief update procedure turns to a nonlinear filtering process, which is addressed using the sigma point filters [6].…”

Section: Introductionmentioning

confidence: 99%

Efficient cooperative localization algorithm in LOS/NLOS environments

Jin

Yin

Fritsche

et al. 2015

2015 23rd European Signal Processing Conference (EUSIPCO)

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The well-known cooperative localization algorithm, 'sumproduct algorithm over a wireless network' (SPAWN) has two major shortcomings, a relatively high computational complexity and a large communication load. Using the Gaussian mixture model with a model selection criterion and the sigma-point (SP) methods, we propose the SPAWN-SP to overcome these problems. The SPAWN-SP easily accommodates different localization scenarios due to its high flexibility in message representation. Furthermore, harsh LOS/NLOS environments are considered for the evaluation of cooperative localization algorithms. Our simulation results indicate that the proposed SPAWN-SP demonstrates high localization accuracy in different localization scenarios, thanks to its high flexibility in message representation.

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Sigma Point Belief Propagation

Cited by 81 publications

References 17 publications

New Efficient Indoor Cooperative Localization Algorithm With Empirical Ranging Error Model

New Efficient Indoor Cooperative Localization Algorithm With Empirical Ranging Error Model

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

Efficient cooperative localization algorithm in LOS/NLOS environments

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