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A novel method for asynchronous source localisation based on time of arrival measurements

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Listed:
  • Huijie Zhu
  • Sheng Liu
  • Zhiqiang Yao
  • Moses Chukwuka Okonkwo
  • Zheng Peng

Abstract

Source localisation is an important component in the application of wireless sensor networks, and plays a key role in environmental monitoring, healthcare and battlefield surveillance and so on. In this article, the source localisation problem based on time-of-arrival measurements in asynchronous sensor networks is studied. Because of imperfect time synchronisation between the anchor nodes and the signal source node, the unknown parameter of start transmission time of signal source makes the localisation problem further sophisticated. The derived maximum-likelihood estimator cost function with multiple local minimum is non-linear and non-convex. A novel two-step method which can solve the global minimum is proposed. First, by leveraging dimensionality reduction, the maximum (minimum) distance maximum (minimum) time-of-arrival matching-based second-order Monte Carlo method is applied to find a rough initial position of the signal source with low computational complexity. Then, the rough initial position value is refined using trust region method to obtain the final positioning result. Comparative analysis with state-of-the-art semidefinite programming and min–max criterion-based algorithms are conducted. Simulations show that the proposed method is superior in terms of localisation accuracy and computational complexity, and can reach the optimality benchmark of Cramér–Rao Lower Bound even in high signal-to-noise ratio environments.

Suggested Citation

  • Huijie Zhu & Sheng Liu & Zhiqiang Yao & Moses Chukwuka Okonkwo & Zheng Peng, 2021. "A novel method for asynchronous source localisation based on time of arrival measurements," International Journal of Distributed Sensor Networks, , vol. 17(10), pages 15501477211, October.
    • Handle: RePEc:sae:intdis:v:17:y:2021:i:10:p:15501477211053706
    DOI: 10.1177/15501477211053706
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    References listed on IDEAS

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    1. Michael B. Giles, 2008. "Multilevel Monte Carlo Path Simulation," Operations Research, INFORMS, vol. 56(3), pages 607-617, June.
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