Localization of Mobile Nodes in Wireless Networks with Correlated in Time Measurement Noise

Mihaylova, Lyudmila; Angelova, Donka; Bull, David; Canagarajah, N.

doi:10.1109/tmc.2010.132

Cited by 54 publications

(45 citation statements)

References 43 publications

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“…This noise involves large errors in RSSI-range models and originates large localization and tracking errors. In recent years some few techniques for target localization under realistic correlated RSSI noise have been proposed [16] but they usually have bad scalability and involve computational loads that can be hardly implemented in COTS WSN nodes.…”

Section: On-line Rssi-range Training Problem Formulationmentioning

confidence: 99%

On-Line RSSI-Range Model Learning for Target Localization and Tracking

Dios

Ollero

Fernandez

et al. 2017

JSAN

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Abstract:The interactions of Received Signal Strength Indicator (RSSI) with the environment are very difficult to be modeled, inducing significant errors in RSSI-range models and highly disturbing target localization and tracking methods. Some techniques adopt a training-based approach in which they off-line learn the RSSI-range characteristics of the environment in a prior training phase. However, the training phase is a time-consuming process and must be repeated in case of changes in the environment, constraining flexibility and adaptability. This paper presents schemes in which each anchor node on-line learns its RSSI-range models adapted to the particularities of its environment and then uses its trained model for target localization and tracking. Two methods are presented. The first uses the information of the location of anchor nodes to dynamically adapt the RSSI-range model. In the second one, each anchor node uses estimates of the target location-anchor nodes are assumed equipped with cameras-to on-line adapt its RSSI-range model. The paper presents both methods, describes their operation integrated in localization and tracking schemes and experimentally evaluates their performance in the UBILOC testbed.

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Section: On-line Rssi-range Training Problem Formulationmentioning

confidence: 99%

On-Line RSSI-Range Model Learning for Target Localization and Tracking

Dios

Ollero

Fernandez

et al. 2017

JSAN

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show abstract

“…Thus, for clarity, we discard subscript τ in all equations, and focus http://asp.eurasipjournals.com/content/2013/1/16 on the static scenario. More details on mobile positioning can be found in [13,14,34]. Due to the high complexity, the presence of nonlinear relationships, and potentially highly non-Gaussian uncertainties, GBP-JT method is not appropriate for cooperative localization [11].…”

Section: Nonparametric Generalized Belief Propagationmentioning

confidence: 99%

Nonparametric generalized belief propagation based on pseudo-junction tree for cooperative localization in wireless networks

Savic

Zazo

2013

EURASIP J. Adv. Signal Process.

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Non-parametric belief propagation (NBP) is a well-known message passing method for cooperative localization in wireless networks. However, due to the over-counting problem in the networks with loops, NBP's convergence is not guaranteed, and its estimates are typically less accurate. One solution for this problem is non-parametric generalized belief propagation based on junction tree. However, this method is intractable in large-scale networks due to the high-complexity of the junction tree formation, and the high-dimensionality of the particles. Therefore, in this article, we propose the non-parametric generalized belief propagation based on pseudo-junction tree (NGBP-PJT). The main difference comparing with the standard method is the formation of pseudo-junction tree, which represents the approximated junction tree based on thin graph. In addition, in order to decrease the number of high-dimensional particles, we use more informative importance density function, and reduce the dimensionality of the messages. As by-product, we also propose NBP based on thin graph (NBP-TG), a cheaper variant of NBP, which runs on the same graph as NGBP-PJT. According to our simulation and experimental results, NGBP-PJT method outperforms NBP and NBP-TG in terms of accuracy, computational, and communication cost in reasonably sized networks.

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“…In fact, the case of white measurement noise is hard to be satisfied and the white measurement noise is only an ideal case. For examples, in wireless communication and global navigation satellite systems, the measurement noises are generally time-correlated (Mihaylova, Angelova, Bull, & Canagarajah, 2011;Petovello et al, 2009;Wang, Li, & Rizos, 2012). When these characteristics of correlated noise are not fully accounted for in the model, unacceptable performance can often result.…”

Section: Introductionmentioning

confidence: 99%

State estimation for discrete-time Markov jump linear systems with time-correlated measurement noise

Liu

2017

Automatica

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Localization of Mobile Nodes in Wireless Networks with Correlated in Time Measurement Noise

Cited by 54 publications

References 43 publications

On-Line RSSI-Range Model Learning for Target Localization and Tracking

On-Line RSSI-Range Model Learning for Target Localization and Tracking

Nonparametric generalized belief propagation based on pseudo-junction tree for cooperative localization in wireless networks

State estimation for discrete-time Markov jump linear systems with time-correlated measurement noise

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