Probabilistic Methods for Location Estimation in Wireless Networks

Kontkanen, Petri; Myllymäki, Petri; Roos, Teemu; Tirri, Henry; Valtonen, Kimmo; Wettig, Hannes

doi:10.1007/0-387-23072-6_11

Cited by 19 publications

(10 citation statements)

References 17 publications

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“…The state corresponds to location and the observation depends only on the current position. Kontkanen et al [74] demonstrated the feasibility of HMM to track the target in the areas of wireless radio networks. When the target was moving at a normal speed, it was possible to observe a series of continuous, dynamic measurements, upon which the location estimation problem could be modelled into a function of time.…”

Section: Hidden Markov Modelsmentioning

confidence: 99%

Improvement Schemes for Indoor Mobile Location Estimation: A Survey

Shang

et al. 2015

Mathematical Problems in Engineering

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Location estimation is significant in mobile and ubiquitous computing systems. The complexity and smaller scale of the indoor environment impose a great impact on location estimation. The key of location estimation lies in the representation and fusion of uncertain information from multiple sources. The improvement of location estimation is a complicated and comprehensive issue. A lot of research has been done to address this issue. However, existing research typically focuses on certain aspects of the problem and specific methods. This paper reviews mainstream schemes on improving indoor location estimation from multiple levels and perspectives by combining existing works and our own working experiences. Initially, we analyze the error sources of common indoor localization techniques and provide a multilayered conceptual framework of improvement schemes for location estimation. This is followed by a discussion of probabilistic methods for location estimation, including Bayes filters, Kalman filters, extended Kalman filters, sigma-point Kalman filters, particle filters, and hidden Markov models. Then, we investigate the hybrid localization methods, including multimodal fingerprinting, triangulation fusing multiple measurements, combination of wireless positioning with pedestrian dead reckoning (PDR), and cooperative localization. Next, we focus on the location determination approaches that fuse spatial contexts, namely, map matching, landmark fusion, and spatial model-aided methods. Finally, we present the directions for future research.

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Section: Hidden Markov Modelsmentioning

confidence: 99%

Improvement Schemes for Indoor Mobile Location Estimation: A Survey

Shang

et al. 2015

Mathematical Problems in Engineering

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“…To estimate the user location, it is assumed that there are W observable signal characteristics, such as received radio signal power and packet error rate, which possess location-dependent statistics or probability distributions [18]. The distributions of …”

Section: Model Descriptionmentioning

confidence: 99%

Probabilistic Cooperative Location Estimation in Wireless Networks

Ouyang¹,

Lan

Lo³

et al. 2010

2010 IEEE Wireless Communication and Networking Conference

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Applications of location estimation have drawn lots of interests in recent years. Many schemes have been developed to estimate the locations of the concerned users. Conventionally, the location of each user is estimated independently by its own observations from the local environment. In this paper, we propose a cooperative location estimation scheme. From the encounter history of a specific user meeting other users and the location estimation results of other users, the user is able to further improve the accuracy of its location estimation. Simulation results show that the cooperative algorithm provides a significant gain over the non-cooperative scheme and the location estimation accuracy increases with the number of users involved in cooperation.This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the WCNC 2010 proceedings.978-1-4244-6398-5/10/$26.00 ©2010 IEEE

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“…Centralized approaches have been proposed to solve this problem for acoustic sources Sheng and Hu [2005] and for sources emitting electromagnetic waves, see, e.g., Kontkanen et al [2004], Gustafsson and Gunnarsson [2005], Gezici et al [2005]. In the first case, some knowledge of the decay rate of the RSS (path loss exponent) is needed for efficient nonlinear least squares estimation.…”

Section: Applicationsmentioning

confidence: 99%

Distributed bounded-error state estimation

Kieffer

2009

IFAC Proceedings Volumes

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This paper presents a distributed bounded-error state estimation algorithm suited, e.g., to measurement processing by a network of sensors. Contrary to centralized estimation, where all data are collected to a central processing unit, here, each data is processed locally by the sensor, the results are broadcasted to the network and taken into account by the other sensors. A first analysis of the conditions under which distributed and centralized estimators provide the same results is presented. An application to the tracking of a moving source using a network of sensors measuring the strength of the signal emitted by the source is considered * . * This work has been partly supported by NEWCOM++

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Probabilistic Methods for Location Estimation in Wireless Networks

Cited by 19 publications

References 17 publications

Improvement Schemes for Indoor Mobile Location Estimation: A Survey

Improvement Schemes for Indoor Mobile Location Estimation: A Survey

Probabilistic Cooperative Location Estimation in Wireless Networks

Distributed bounded-error state estimation

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