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Augmented consensus filter for simultaneous localization and tracking with limited sense range

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Abstract

In this paper, we investigate how to exploit distributed average consensus fusion for conducting simultaneous localization and tracking (SLAT) by using wireless sensor networks. To this end, we commence by establishing a limited sense range (LSR) nonlinear system that characterizes the coupling of target state and sensor localization with respect to each sensor. We then employ an augmented extended Kalman filter to estimate the sensor and target states of our system. Furthermore, we adopt a consensus filtering scheme which fuses the information from neighboring sensors. We thus obtain a two-stage distributed filtering framework that not only obtains updated sensor locations trough augment filtering but also provides an accurate target state estimate in consensus filtering. Additionally, our framework is computationally efficient because it only requires neighboring sensor communications. The simulation results reveal that the proposed filtering framework is much more robust than traditional information fusion methods in limited ranging conditions.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant nos. 61503413 and 61411130134), Shandong Provincial Natural Science Foundation (Grant no. ZR2015FL027), Shandong Outstanding Young Scientist Fund (Grant no. BS2013DX006) and the Fundamental Research Funds for the Central Universities (Grant no. 24720142087A).

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Authors and Affiliations

  1. College of Information and Control Engineering, China University of Petroleum (East China), Qingdao, China

    Xiangyuan Jiang & Peng Ren

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  1. Xiangyuan Jiang
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  2. Peng Ren
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Correspondence to Peng Ren.

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Jiang, X., Ren, P. Augmented consensus filter for simultaneous localization and tracking with limited sense range. Ann. Telecommun. 71, 657–664 (2016). https://doi.org/10.1007/s12243-016-0536-5

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  • DOI: https://doi.org/10.1007/s12243-016-0536-5

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