Source Localization and Tracking Using Distributed Asynchronous Sensors

Li, Teng; Ekpenyong, A.; Huang, Yih-Fang

doi:10.1109/tsp.2006.880213

Cited by 77 publications

(58 citation statements)

References 20 publications

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“…Recently, source localization using wireless sensor networks (WSNs) has attracted great research efforts [1][2][3][4]. To determine the source position, positioning parameters such as time of arrival (TOA), time difference of arrival (TDOA), received signal strength (RSS), and angle of arrival (AOA) are commonly used.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

On the Joint Time Synchronization and Source Localization Using TOA Measurements

Ming

Yang

2013

International Journal of Distributed Sensor Networks

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“…To obtain accurate location estimates of sensor nodes, range-based localization algorithms are more favorable than range-free ones [3][4][5]. In general, the range-based algorithms always follow two steps [6,7]: they first measure some metrics bearing location information, the so-called ranging or bearing, and second estimate the positions based on those metrics, the so-called location information fusion. There are mainly four metrics: time-of-arrival (TOA) or time-of-flight (TOF) [8], time-difference-of-arrival (TDOA) [5,6], angleof-arrival (AOA) [9], and received signal strength (RSS) [10].…”

Section: Introductionmentioning

confidence: 99%

Robust TDOA Localization Algorithm for Asynchronous Wireless Sensor Networks

Huagang

Chen

et al. 2015

International Journal of Distributed Sensor Networks

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This paper presents a tag localization algorithm based on the time-difference-of-arrival (TDOA) of mobile tag signal for asynchronous wireless sensor network (WSN) with N anchors (nodes with known locations) and a large number of mobile tags. To obtain time synchronization, all anchors broadcast signals periodically; relative clock offsets and skews of anchor pairs are estimated by the least-square (LS) method using the times-of-arrival (TOAs) of broadcast signals at anchors. When a tag transmits signal, the TOA of tag signal at each anchor is stamped and errors in original TDOAs of tag signal due to relative clock offsets and skews of anchor pairs are eliminated. Based on Gaussian noise model, maximum likelihood estimation (MLE) for the tag position is obtained. Performance issues are addressed by evaluating the Cramér-Rao lower bound of synchronization and localization algorithms. Since the tag can be located via a single transmission, least power consumption of tag is required, and large number of tags can be served in WSN. The proposed algorithm is simple and effective, with performance close to that of synchronous TDOA algorithm.

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“…Tracking a mobile target node is an important issue in many wireless sensor network (WSN) applications [1,2,3]. In general, tracking systems follow two steps.…”

Section: Introductionmentioning

confidence: 99%

“…High accuracy and potentially low cost implementation make TOA or TDOA based on ultra-wideband impulse radios (UWB-IRs) a promising ranging method [4]. Consequently, clock synchronization has to be taken into account for a localization or a tracking system using TOA or TDOA measurements [2,3,5]. In the second step, the ranging measurements are used to track the target position.…”

Section: Introductionmentioning

confidence: 99%

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Tracking a mobile node by asynchronous networks

Wang

Leus

2011

2011 IEEE 12th International Workshop on Signal Processing Advances in Wireless Communications

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In this paper, we propose a Kalman filter (KF) based tracking approach to track a target node with the assistance of anchors in an asynchronous network with clock offsets. We employ the asymmetric trip ranging (ATR) protocol to obtain TOA measurements and facilitate clock offset cancellation, and further derive a linear measurement model from the TOA measurements. Thus, the KF based on this linear measurement model does not have the modeling errors inherently contained in the Extended Kalman filter (EKF). Furthermore, low computational complexity makes the proposed KF a promising solution for practical use. We compare the proposed KF with the EKF. The simulation results corroborate its efficiency.

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Source Localization and Tracking Using Distributed Asynchronous Sensors

Cited by 77 publications

References 20 publications

On the Joint Time Synchronization and Source Localization Using TOA Measurements

On the Joint Time Synchronization and Source Localization Using TOA Measurements

Robust TDOA Localization Algorithm for Asynchronous Wireless Sensor Networks

Tracking a mobile node by asynchronous networks

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