Wireless Positioning Systems: Operation, Application, and Comparison

Zekavat, S.A.; Kansal, Stuti; Levesque, Allen H.

doi:10.1002/9781119434610.ch1

Cited by 11 publications

(13 citation statements)

References 24 publications

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“…RSS‐based localization estimates for node networks tracking animal movements are attractive for their simplicity, with the only information needed being the location of nodes, signal strength of a received radio signal, and the time that a signal is received. Thus, using an RSS‐based localization method requires minimal hardware (Zekavat et al, 2019 ), allowing for relative ease of deployment of large multi‐node networks with minimal upfront costs to simultaneously track the movements of multiple animals within the geographic area of a network. Our approach of using filters to limit which nodes are used in RSS‐based localization estimates greatly improved accuracy, but further improvement in location accuracy could be gained by coupling other analytical strategies with our methods.…”

Section: Discussionmentioning

confidence: 99%

Optimizing trilateration estimates for tracking fine‐scale movement of wildlife using automated radio telemetry networks

Paxton

Baker

Crytser

et al. 2022

Ecology and Evolution

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A major advancement in the use of radio telemetry has been the development of automated radio tracking systems (ARTS), which allow animal movements to be tracked continuously. A new ARTS approach is the use of a network of simple radio receivers (nodes) that collect radio signal strength (RSS) values from animal‐borne radio transmitters. However, the use of RSS‐based localization methods in wildlife tracking research is new, and analytical approaches critical for determining high‐quality location data have lagged behind technological developments. We present an analytical approach to optimize RSS‐based localization estimates for a node network designed to track fine‐scale animal movements in a localized area. Specifically, we test the application of analytical filters (signal strength, distance among nodes) to data from real and simulated node networks that differ in the density and configuration of nodes. We evaluate how different filters and network configurations (density and regularity of node spacing) may influence the accuracy of RSS‐based localization estimates. Overall, the use of signal strength and distance‐based filters resulted in a 3‐ to 9‐fold increase in median accuracy of location estimates over unfiltered estimates, with the most stringent filters providing location estimates with a median accuracy ranging from 28 to 73 m depending on the configuration and spacing of the node network. We found that distance filters performed significantly better than RSS filters for networks with evenly spaced nodes, but the advantage diminished when nodes were less uniformly spaced within a network. Our results not only provide analytical approaches to greatly increase the accuracy of RSS‐based localization estimates, as well as the computer code to do so, but also provide guidance on how to best configure node networks to maximize the accuracy and capabilities of such systems for wildlife tracking studies.

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Section: Discussionmentioning

confidence: 99%

Optimizing trilateration estimates for tracking fine‐scale movement of wildlife using automated radio telemetry networks

Paxton

Baker

Crytser

et al. 2022

Ecology and Evolution

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“…Localization and positioning systems are very popular topics in the scientific community, given their relevance to many telecommunication applications. Positioning is an enabling factor for many activities, such as supporting users' everyday life through navigation assistance systems [1], emergency response facilitation, collision avoidance in autonomous driving, defense purposes, and multirobot coordination tasks [2]. An alternative field of application for localization systems is telecommunications [3].…”

Section: Introductionmentioning

confidence: 99%

“…When dealing with this inversion, even small errors in the phase estimation step can corrupt the precision of the AoA value. In fact, let us suppose that a small phase estimation error δ is added to the real value (2). The AoA ϑ estimation θij becomes…”

Section: Introductionmentioning

confidence: 99%

A Linear Technique for Artifacts Correction and Compensation in Phase Interferometric Angle of Arrival Estimation

Florio

Avitabile

Coviello

2022

Sensors

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Radio localization and radio positioning are relevant research fields for many telecommunications technologies. Usually, the solutions proposed by the literature rely on adaptive techniques related to some parameters that can be extracted from the received signal in cooperative device tracking. In this paper, we explore the artifacts that may be introduced into Angle-of-Arrival estimation based on phase interferometry, and we introduce a simple technique to mitigate their impact. Details of the mathematical discussion are presented and the approach is experimentally validated. The experimental results are compared with raw data to demonstrate the effectiveness of the proposed technique.

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“…Active positioning is characterized by all-weather and high-speed positioning, which is widely used in radio-frequency identification (RFID) [2], traffic alert and collision avoidance systems (TCAS) [3] and other applications. Unlike active positioning, where radio signals are emitted by transmitters, passive positioning has received considerable attention due to good concealment, strong antiinterference ability and low cost in recent years and is applied in Global Positioning System (GPS), wireless sensor networks (WSNs), tracking radars and vision system [4]. In particular, localization in WSNs has a broad spectrum of applications in fields such as disaster monitoring, smart home, indoor navigation and animal tracking [5]- [8].…”

Section: Introductionmentioning

confidence: 99%

RSS-based Multiple Sources Localization with Unknown Log-normal Shadow Fading

Chu¹,

Guo²,

You³

et al. 2021

Preprint

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Multi-source localization based on received signal strength (RSS) has drawn great interest in wireless sensor networks. However, the shadow fading term caused by obstacles cannot be separated from the received signal, which leads to severe error in location estimate. In this paper, we approximate the log-normal sum distribution through Fenton-Wilkinson method to formulate a non-convex maximum likelihood (ML) estimator with unknown shadow fading factor. In order to overcome the difficulty in solving the non-convex problem, we propose a novel algorithm to estimate the locations of sources. Specifically, the region is divided into 𝑁 grids firstly, and the multi-source localization is converted into a sparse recovery problem so that we can obtain the sparse solution. Then we utilize the K-means clustering method to obtain the rough locations of the offgrid sources as the initial feasible point of the ML estimator. Finally, an iterative refinement of the estimated locations is proposed by dynamic updating of the localization dictionary. The proposed algorithm can efficiently approach a superior local optimal solution of the ML estimator. It is shown from the simulation results that the proposed method has a promising localization performance and improves the robustness for multisource localization in unknown shadow fading environments. Moreover, the proposed method provides a better computational complexity from 𝑂 (𝐾 3 𝑁 3 ) to 𝑂 (𝑁 3 ).

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Wireless Positioning Systems: Operation, Application, and Comparison

Cited by 11 publications

References 24 publications

Optimizing trilateration estimates for tracking fine‐scale movement of wildlife using automated radio telemetry networks

Optimizing trilateration estimates for tracking fine‐scale movement of wildlife using automated radio telemetry networks

A Linear Technique for Artifacts Correction and Compensation in Phase Interferometric Angle of Arrival Estimation

RSS-based Multiple Sources Localization with Unknown Log-normal Shadow Fading

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