Improving Range Accuracy of IEEE 802.15.4a Radios In the Presence of Clock Frequency Offsets

Şahinoğlu, Zafer

doi:10.1109/lcomm.2011.122010.102034

Cited by 19 publications

(23 citation statements)

References 8 publications

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“…The proposed algorithm accomplishes the joint synchronization and source localization task by evaluating sequentially (24), (25), (26), and (29). Note that in computinĝ1 and̂2 using (24) and (25), the true source position p is needed to produce the weighting matrices W 1 and W 2 . To bypass these difficulties, (24), we first set W 1 to be an identity matrix of appropriate dimension and computê1 to find 1 .…”

Section: Algorithm and Performance Analysismentioning

confidence: 99%

“…Then, (24) is calculated again with p in W 1 being replaced bŷ 1 (1 : 2) that is just obtained. In evaluating (25), the source position estimate from Stage 1 of Step 1 processing is utilized to generate W 2 .…”

Section: Algorithm and Performance Analysismentioning

confidence: 99%

“…The unbiasedness ofp can be shown as follows. The estimation error inp can be written as, from the definitions of Δp, Δ̂2 and Δ̂1 given below (26), (25), and (24),…”

Section: Performance Analysismentioning

confidence: 99%

“…TOF is usually considered as an inexpensive but effective alternative to obtain TOAs without using the cumbersome and costly source-sensor synchronization [24][25][26]. In this case, however, the turn-around time at the source needs to be either estimated [26] or small enough to be negligible [27,28] so that TOA positioning can be utilized.…”

Section: Introductionmentioning

confidence: 99%

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On the Joint Time Synchronization and Source Localization Using TOA Measurements

Ming

Yang

2013

International Journal of Distributed Sensor Networks

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This paper considers the problem of estimating the clock bias and the position of an unknown source using time of arrival (TOA) measurements obtained at a sensor array to achieve time synchronization and source localization. The study starts with deriving the localization mean square error (MSE) for the case where we pretend that the source clock bias is absent and apply TOA positioning to find the source position. An upper bound on the clock bias, over which we shall obtain a higher localization MSE than that from jointly identifying the clock bias with the source position, is established. Motivated by the MSE analysis, this paper proceeds to develop a new efficient solution for joint synchronization and source localization. The new method is in closedform, computationally attractive, and more importantly; it is shown analytically to attain the CRLB accuracy under small Gaussian TOA measurement noise. Computer simulations are conducted to corroborate the theoretical development and illustrate the good performance of the proposed algorithm.

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Section: Algorithm and Performance Analysismentioning

confidence: 99%

Section: Algorithm and Performance Analysismentioning

confidence: 99%

“…The unbiasedness ofp can be shown as follows. The estimation error inp can be written as, from the definitions of Δp, Δ̂2 and Δ̂1 given below (26), (25), and (24),…”

Section: Performance Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the Joint Time Synchronization and Source Localization Using TOA Measurements

Ming

Yang

2013

International Journal of Distributed Sensor Networks

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“…A target node transmits a signal to a reference node located at a known position and the reference node responds to the received signal after an unknown turn-around time delay. As it is common in the literature, we assume that the reference node measures the turn-around time by a loop back test and transmits the estimate to the target node [13,14]. The target node then computes the round-trip delay based on an estimate of the turn-around time.…”

Section: Introductionmentioning

confidence: 99%

Range based sensor node localization in the presence of unknown clock skews

Gholami

Gezici

Ström

2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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We deal with the positioning problem based on two-way time-ofarrival (TW-TOA) measurements in asynchronous wireless sensor networks. The optimal estimator for this problem poses a difficult global optimization problem. To avoid the drawbacks in solving the optimal estimator, we use approximations and derive linear models, which facilitate efficient solutions. In particular, we employ the least squares method and solve a general trust region subproblem to find a coarse estimate. To further refine the estimate, we linearize the measurements and obtain a linear model which can be solved using regularized least squares. Simulation results illustrate that the proposed approaches asymptotically attain the Cramér-Rao lower bound.

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Cooperative Positioning in Wireless Networks

Gholami

Keskin

Gezici

et al. 2016

Wiley Encyclopedia of Electrical and Electronics Engineering

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In this article, we study cooperative positioning in wireless networks in which target nodes at unknown locations locally collaborate with each other to find their locations. We review different models available for positioning and categorize the model‐based algorithms in two groups: centralized and distributed. We then investigate a lower bound on the variance of unbiased estimators, namely the Cramer–Rao lower bound, which is a common benchmark in the positioning literature. We finally discuss some open problems and research topics in the area of positioning that are worth exploring in future studies.

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Improving Range Accuracy of IEEE 802.15.4a Radios In the Presence of Clock Frequency Offsets

Cited by 19 publications

References 8 publications

On the Joint Time Synchronization and Source Localization Using TOA Measurements

On the Joint Time Synchronization and Source Localization Using TOA Measurements

Range based sensor node localization in the presence of unknown clock skews

Cooperative Positioning in Wireless Networks

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