Target Positioning with GDOP Assisted Nodes Selection Algorithm in Wireless Sensor Networks

Zhang, Yunzhou; Wei, Dongfei; Fu, Wenyan; Yang, Bing

doi:10.1155/2014/404812

Cited by 12 publications

(6 citation statements)

References 26 publications

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“…Third, theoretically, the more the nodes involved in the calculation, the lower the GDOP value of the combination will be, which represents a higher positioning accuracy. In addition, In the traditional GDOP‐based nodes selection algorithm, n nodes will be selected from m anchor nodes, and therefore, in order to select a subset that has the smallest GDOP, matrix multiplication and inversion should be executed for a combination of m to n times . Finally, some accuracy results are provided to have a reference about performance obtained with GDOP.…”

Section: Resultsmentioning

confidence: 99%

“…Finally, some accuracy results are provided to have a reference about performance obtained with GDOP. For instance, the best positioning accuracy obtained in the work of Zhang et al is around 1 m when 10 anchor nodes are involved in the system. Another example of GDOP is presented in the work of Sharp et al, where it is assumed that there is no mobile target but all anchor nodes will act as mobile units.…”

Section: Resultsmentioning

confidence: 99%

“…Zhang et al presented a geometric dilution of precision (GDOP) assisted nodes selection (GANS) algorithm for calculating the GDOP value of the current geometric distribution. As the evaluation criteria, sensor nodes contribution to the total GDOP value is adopted.…”

Section: Survey Of Indoor Positioning Systemsmentioning

confidence: 99%

See 2 more Smart Citations

Anchor selection for UWB indoor positioning

Albaidhani

Morell

Vicario

2019

Trans Emerging Tel Tech

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On the positioning accuracy, the geometric distribution of anchor nodes in wireless sensor networks has notable impacts. To select the optimum node combination, conventional methods that depend on geometric dilution of precision demand to spend time on calculating every possible combination of nodes. In military urban and emergency response operations, the time is a crucial issue, and a precise positioning system with a clear indoor covering is a highly prerequisite tool to enhance the safety. It should be seamless, low, frugal, power efficacious, low cost, and supply less meter‐level accuracy. In this paper, the main goal is to reduce the anchors installation time and to obtain a precise localization system. To obtain this goal, a novel algorithm to build an accurate indoor positioning system is created using a mean square error (MSE) of an estimated position of a mobile station (MS) located by different groups of installed anchor nodes online using least square (LS) method then selecting the group having less MSE value (anchor selection method) to relocate the MS using a weighted LS method. The results were highly acceptable for indoor localization because the module attained an MSE localization accuracy in a hard non–line of sight environment below 0.5 m2 also the time of installing the anchor nodes will be reduced. This paper includes the description of the algorithm and the results of the conducted experiments.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Anchor selection for UWB indoor positioning

Albaidhani

Morell

Vicario

2019

Trans Emerging Tel Tech

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“…WSN is composed of some mobile nodes and a large number of anchor nodes through the self-organization and multihop methods [8]. In order to achieve continuous indoor positioning, some scholars put forward the INS/WSN integrated positioning system [9].…”

Section: Introductionmentioning

confidence: 99%

Integrated SINS/WSN Positioning System for Indoor Mobile Target Using Novel Asynchronous Data Fusion Method

Yang

Luo

et al. 2017

Journal of Sensors

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According to the asynchronous transmission of data for the SINS/WSN integrated positioning system, this paper proposes a novel asynchronous data fusion method using Unscented Kalman Filter for SINS/WSN integrated positioning system based on indoor mobile target. The state equation of the integrated system is built with the motion characteristic of mobile target. The pseudo measurement equation is built based on the time sequence of SINS/WSN measured value through detecting the measurement of WSN in every fusion period. Considering that the improved state-space model, comprised of the state equation and pseudo measurement equation, is the nonlinear equations, the Unscented Kalman Filter is applied to estimate the state value of the statespace model. Hence the asynchronous data fusion method for SINS/WSN integrated positioning system can be achieved. Simulation results and experimental tests show that the positioning system with proposed asynchronous data fusion algorithm performs feasibility and stability under circumstances of the asynchronous time, and it is superior to the traditional asynchronous data fusion and synchronous data fusion methods.

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“…Only the sensor systems which are in range of the target transmit their positioning information; all other sensor systems are inactive and do not transmit data. This is taken a step further in [4] where every sensor system that is within range of the target is a candidate for participation in the target position estimation task. The sensor systems are still static and those not participating in the tracking task are still inactive, but only the sensor system combination that yields the most accurate position estimate is used in the tracking process rather than every node within range of the target.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Sensor Locations in a Multisensor Single-Object Tracking System

Cashbaugh

Kitts

2015

International Journal of Distributed Sensor Networks

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Tracking a mobile object presents many challenges, especially when the tracked object is autonomous or semiautonomous and may move unpredictably. The use of autonomous mobile sensor systems allows for greater opportunity to track the mobile object but does not always yield an estimate of the tracked object's location that minimizes the estimation error. This paper presents a methodology to optimize the sensor system locations, given a single object and a fixed number of sensor systems, to achieve a position estimate that minimizes the estimation error. The tracking stations may then be controlled to achieve and maintain this optimal position, under position constraints. The theory predicts that given n sensor systems and one object there is a sensor system configuration that will yield a position estimate that minimizes the estimation error. A mathematical basis for this theory is presented and simulation and experimental results for two and three sensor system cases are shown to illustrate the effectiveness of the theory in the laboratory.

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Target Positioning with GDOP Assisted Nodes Selection Algorithm in Wireless Sensor Networks

Cited by 12 publications

References 26 publications

Anchor selection for UWB indoor positioning

Anchor selection for UWB indoor positioning

Integrated SINS/WSN Positioning System for Indoor Mobile Target Using Novel Asynchronous Data Fusion Method

Optimizing Sensor Locations in a Multisensor Single-Object Tracking System

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