AOA Assisted NLOS Error Mitigation for TOA-Based Indoor Positioning Systems

Akgül, Ferit Ozan; Pahlavan, Kaveh

doi:10.1109/milcom.2007.4454868

Cited by 10 publications

(7 citation statements)

References 13 publications

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“…Node localization has been studied extensively over the years, with most algorithms requiring predetermined reference nodes in the localization process [Kanaan and Pahlavan 2004;Akgul and Pahlavan 2007;Vieira et al 2010]. A survey on the state-ofthe-art in underwater localization can be found in Tan et al [2011].…”

Section: Node Localizationmentioning

confidence: 99%

“…On the other hand, in Akgul and Pahlavan [2007], the AOA-assisted NLOS approach is able to use a fixed reference node and a reflected wall to determine the location of a moving receiver by using the AOA and TOA of the reflected path to reconstruct the LOS lost due to an obstacle interfering with LOS communication. A drawback to this approach is that the transmitter and receiver must be initially connected through a LOS connection before an obstacle interferes with the connection, causing the receiver to use the AOA of the reflected path to reconstruct the LOS connection.…”

Section: Node Localizationmentioning

confidence: 99%

“…A drawback to this approach is that the transmitter and receiver must be initially connected through a LOS connection before an obstacle interferes with the connection, causing the receiver to use the AOA of the reflected path to reconstruct the LOS connection. Our SBR-AL differs from both Kanaan and Pahlavan [2004] and Akgul and Pahlavan [2007] in two main aspects: first, our approach uses vectors instead of explicit TOA estimation for ranging, and second our approach does not rely on anchor nodes to perform multiple range measurements. A phero-trail location service protocol was demonstrated in Vieira et al [2010], where the path of the mobile sink node is projected onto a 2D plane on the water surface consisting of sensor nodes.…”

Section: Node Localizationmentioning

confidence: 99%

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Surface-Reflection-Based Communication and Localization in Underwater Sensor Networks

Emokpae

Younis

2014

ACM Trans. Sen. Netw.

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Most communication and localization algorithms in underwater environments have been constrained by dependencies on the Line Of Sight (LOS), which is hard to guarantee due to the inherent node mobility. This constraint hinders node discovery and ad hoc formation in underwater networks and limits the performance of routing protocols. This article introduces a novel Surface-Based Reflection (SBR) model that uses a homomorphic deconvolution technique to establish water-surface-reflected communication links. We then propose a Surface-Based Reflection Anchor-free Localization (SBR-AL) algorithm that can be employed by the individual nodes to establish a relative coordinate system. Our approach also employs a switchbeamed directional antenna model that allows each node to use the LOS estimated from SBR-AL to enable directional communication which is beneficial for higher Signal-to-Noise Ratios (SNR). The relative locations can facilitate the various network operation functions such as geo-routing and collision-free medium access. The simulation results confirm the effectiveness of the proposed approach. ACM Reference Format:Lloyd Emokpae and Mohamed Younis. 2014. Surface-reflection-based communication and localization in underwater sensor networks.

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Section: Node Localizationmentioning

confidence: 99%

Section: Node Localizationmentioning

confidence: 99%

Section: Node Localizationmentioning

confidence: 99%

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Surface-Reflection-Based Communication and Localization in Underwater Sensor Networks

Emokpae

Younis

2014

ACM Trans. Sen. Netw.

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“…A major drawback to this algorithm is the dependency on the reference nodes and the topology requirement; in most indoor and underwater environments it will not be feasible to establish many reference nodes (especially in a grid topology) due to cost, energy and operational requirements. On the other hand, in [8] the AOA assisted NLOS approach was able to use a fixed reference node and a reflected wall to determine the location of a moving receiver by using the AOA and TOA of the reflectedpath to reconstruct the lost LOS due to an obstacle interfering with the LOS communication. A drawback to this approach is that the transmitter and receiver must be initially connected through a LOS connection before an obstacle interferes with the connection causing the receiver to use the AOA of the reflected path to reconstruct the LOS connection.…”

Section: Related Workmentioning

confidence: 99%

Surface Based Anchor-Free Localization Algorithm for Underwater Sensor Networks

Emokpae

Younis

2011

2011 IEEE International Conference on Communications (ICC)

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Localization in underwater environments has been constrained by the dependencies on the line of sight (LOS) due to the challenging variability's of the environment. This dependency hinders node discovery and ad-hoc formation in underwater networks and limits the performance of routing protocols. Most proposed algorithms in the literature rely on anchor nodes that are at fixed positions to serve as reference points, which is not practical in many applications. This paper introduces a novel approach to the localization problem that allows for node discovery without depending on the LOS and any fixed reference nodes. In the proposed surface-based reflection anchor-free localization (SBRAL) algorithm all nodes will apply homomorphic deconvolution to establish a water-surfacereflected communication link. SBRAL then creates a relative coordinate system where every node in the network identifies others nodes by increasing the SBRAL transmit angle and using the reflection points on the water surface as temporary reference points. The simulation results confirm the effectiveness of the proposed SBRAL algorithm.

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“…Spencer's Laplacian model [2] is particularly useful for MIMO telecommunications applications in which data rate and coverage are important factors hence relative positions of the transmitter and receiver is not of primary concern. When indoor geolocation applications are considered, AOA information can be used to increase ranging and hence positioning accuracy coupled with additional information such as TOA [3]. AOA information becomes particularly useful in tracking certain indirect MPCs when these MPCs can be used to aid in precise TOA ranging when the LOS path gets blocked due to obstructions [4,5].…”

Section: Introductionmentioning

confidence: 99%

A Novel Statistical AOA Model Pertinent to Indoor Geolocation

Akgül¹,

Pahlavan²

2010

JGIS

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Abstract:A novel statistical angle-of-arrival (AOA) model for indoor geolocation applications is presented. The modeling approach focuses on the arrivals of the multipath components with respect to the line-of-sight (LOS) path which is an important component especially when indoor geolocation applications are considered. The model is particularly important for indoor applications where AOA information could be utilized for tracking indirect paths to aid in precise ranging in harsh and dense multipath environments where LOS path might be blocked due to obstructions. The results have been obtained by a measurement calibrated ray-tracing (RT) tool.

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AOA Assisted NLOS Error Mitigation for TOA-Based Indoor Positioning Systems

Cited by 10 publications

References 13 publications

Surface-Reflection-Based Communication and Localization in Underwater Sensor Networks

Surface-Reflection-Based Communication and Localization in Underwater Sensor Networks

Surface Based Anchor-Free Localization Algorithm for Underwater Sensor Networks

A Novel Statistical AOA Model Pertinent to Indoor Geolocation

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