Near-Optimal Location Tracking Using Sensor Networks

Sharma, Gokarna; Krishnan, Hari; Busch, Costas; Brandt, Steven R.

doi:10.1109/ipdpsw.2014.85

Cited by 2 publications

(1 citation statement)

References 31 publications

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“…Furthermore, many problems in the real world cannot be represented by linear models. While the EKF, UKF, and other derivatives of KF attempt to address these problems, their adaptability for tracking in the real world still requires further improvement [25,26]. On the other hand, while PF can flexibly adapt to the nonlinear dynamic model and multimodal observation model, the particle degradation phenomenon leads to a decline of the tracking accuracy [25,27].…”

Section: Introductionmentioning

confidence: 99%

SVM+KF Target Tracking Strategy Using the Signal Strength in Wireless Sensor Networks

Wang

Liu

Wang

et al. 2020

Sensors

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Target Tracking (TT) is a fundamental application of wireless sensor networks. TT based on received signal strength indication (RSSI) is by far the cheapest and simplest approach, but suffers from a low stability and precision owing to multiple paths, occlusions, and decalibration effects. To address this problem, we propose an innovative TT algorithm, known as the SVM+KF method, which combines the support vector machine (SVM) and an improved Kalman filter (KF). We first use the SVM to obtain an initial estimate of the target’s position based on the RSSI. This enhances the ability of our algorithm to process nonlinear data. We then apply an improved KF to modify this estimated position. Our improved KF adds the threshold value of the innovation update in the traditional KF. This value changes dynamically according to the target speed and network parameters to ensure the stability of the results. Simulations and real experiments in different scenarios demonstrate that our algorithm provides a superior tracking accuracy and stability compared to similar algorithms.

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Section: Introductionmentioning

confidence: 99%

SVM+KF Target Tracking Strategy Using the Signal Strength in Wireless Sensor Networks

Wang

Liu

Wang

et al. 2020

Sensors

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Target Tracking for Sensor Networks

2016

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Target-tracking algorithms typically organize the network into a logical structure (e.g., tree, cluster, or faces) to enable data fusion and reduce communication costs. These algorithms often predict the target’s future position. In addition to using position forecasts for decision making, we can also use such information to save energy by activating only the set of sensors nearby the target’s trajectory. In this work, we survey of the state of the art of target-tracking techniques in sensor networks. We identify three different formulations for the target-tracking problem and classify the target-tracking algorithms based on common characteristics. Furthermore, for the sake of a better understanding of the target-tracking process, we organize this process in six components: target detection, node cooperation, position computation, future-position estimation, energy management, and target recovery. Each component has different solutions that affect the target-tracking performance.

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Near-Optimal Location Tracking Using Sensor Networks

Cited by 2 publications

References 31 publications

SVM+KF Target Tracking Strategy Using the Signal Strength in Wireless Sensor Networks

SVM+KF Target Tracking Strategy Using the Signal Strength in Wireless Sensor Networks

Target Tracking for Sensor Networks

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