Network Experimentation for Cooperative Localization

Conti, Andrea; Guerra, Matteo; Dardari, Davide; Decarli, Nicolò; Win, Moe Z.

doi:10.1109/jsac.2012.120227

Cited by 249 publications

(159 citation statements)

References 49 publications

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“…Although it has been recently spotlighted in [23], [220] and experimentally evaluated in [221], network cooperation in the distributed estimation has been already long studied in [222]. Localization in cooperative ad-hoc network has been shown in [223] where the TOA and AOA measurements are combined in a distributed estimation framework.…”

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

355

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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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

355

171

View full text Add to dashboard Cite

show abstract

“…In cooperative localization, sensors work together in a peer-to-peer manner to make measurements and then form a map of the network. Another paper, where the authors introduce the notion of network experimentation and propose an experimentation methodology particularly suited for cooperative localization wireless networks, is [26]. This study enables the performance evaluation of various network localization algorithms under a common setting.…”

Section: Related Workmentioning

confidence: 99%

“…Works [25][26][27][28] are connected with this work because the cooperation between nodes inside a wireless location system are crucial to achieve the goal of reducing the error for indoor location. The main difference with our work is that these systems do not try to study the people's behavior and whether or not their mobility follows a pattern.…”

Section: Related Workmentioning

confidence: 99%

Stochastic Cooperative Decision Approach for Studying the Symmetric Behavior of People in Wireless Indoor Location Systems

et al. 2016

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Nowadays, several wireless location systems have been developed in the research world. The goal of these systems has always been to find the greatest accuracy as possible. However, if every node takes data from the environment, we can gather a lot of information, which may help us understand what is happening around our network in a cooperative way. In order to develop this cooperative location and tracking system, we have implemented a sensor network to capture data from user devices. From this captured data we have observed a symmetry behavior in people's movements at a specific site. By using these data and the symmetry feature, we have developed a statistical cooperative approach to predict the new user's location. The system has been tested in a real environment, evaluating the next location predicted by the system and comparing it with the next location in the real track, thus getting satisfactory results. Better results have been obtained when the stochastic cooperative approach uses the transition matrix with symmetry.

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“…Moreover, the distribution is generally non-Gaussian in harsh environments due to the existence of NLOS and multipath [51], [52]. 17 Notice that BC can be performed simply by evaluating some characteristics of , e.g., statistical moments and percentiles, as shown in Section III.…”

Section: B Measurements Modelsmentioning

confidence: 99%

Belief Condensation Filtering

Mazuelas

Shen

Win

2013

IEEE Trans. Signal Process.

Self Cite

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Abstract-Inferring a sequence of variables from observations is prevalent in a multitude of applications. Traditional techniques such as Kalman filters (KFs) and particle filters (PFs) are widely used for such inference problems. However, these techniques fail to provide satisfactory performance in many important nonlinear or non-Gaussian scenarios. In addition, there is a lack of a unified methodology for the design and analysis of different filtering techniques. To address these problems, in this paper, we propose a new filtering methodology called belief condensation (BC) filtering. First, we establish a general framework for filtering techniques and propose an optimality criterion that leads to BC filtering. We then propose efficient BC algorithms that can best represent the complex distributions arising in the filtering process. The performance of the proposed techniques is evaluated for two representative nonlinear/non-Gaussian problems, showing that the BC filtering can provide accuracy approaching the theoretical bounds and outperform existing techniques in terms of the accuracy versus complexity tradeoff.

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Network Experimentation for Cooperative Localization

Cited by 249 publications

References 49 publications

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

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

Stochastic Cooperative Decision Approach for Studying the Symmetric Behavior of People in Wireless Indoor Location Systems

Belief Condensation Filtering

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