An efficient ranging method based on Chinese remainder theorem for RIPS measurement
The radio interferometric positioning system (RIPS) measures the phase difference of the interference signal to provide high accuracy and at the same time maintain simple hardware configuration for wireless sensor networks. However, it suffers from phase ambiguity problem because of the periodicity of phase, which makes it hard to determine the actual distance difference only from a single phase measurement. To solve the problem, RIPS makes multiple measurements at different frequencies so as to determine the distance difference from multiple phases. However, this is a computationally intensive searching process and not suitable for energy-constrained wireless sensor nodes. In this paper, we introduce the Chinese remainder theorem (CRT) to RIPS to solve the phase ambiguity problem. Meanwhile, we utilize some properties of the coefficients in the CRT algorithm to avoid the over-sensitivity of the traditional CRT, which increases the robustness of the algorithm. We apply this robust CRT algorithm to the ranging process which calculates the distance difference directly from a closed-form equation and therefore reduces the response time and the energy consumption of the ranging procedure.
Keywordsranging phase ambiguity Chinese remainder theorem robustness energy efficiency wireless sensor networks
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- 1.He T, Huang C, Blum B M, et al. Range-free localization schemes for large scale sensor networks. In: Proceeding of 9th International Conference on Mobile Computing and Networking (MobiCom’03), San Diego, CA, 2003. 81–95Google Scholar
- 2.Girod L, Estrin D. Robust range estimation using acoustic and multimodal sensing. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’01). Maui, Hawaii, IEEE Robotics and Automation Society, 2001. 3: 1312–1320Google Scholar
- 3.Priyantha B N. The Cricket indoor location system. Dissertation for the Doctoral Degree, MA, Massachusetts Institute of Technology, 2005. 189–190Google Scholar
- 4.Maroti M, Kusy B, Balogh G, et al. Radio interferometric geolocation. In: Proceedings of 3rd International Conference on Embedded Networked Sensor Systems, (SenSys’05), San Diego, CA, USA, 2005. 1–12Google Scholar
- 5.Lucarelli D, Saksena A, Farrell R, et al. Distributed inference for network localization using radio interferometric ranging. In: 5th European Conference on Wireless Sensor Networks (EWSN 2008), Bologa, Italy: Springer, 2008. 52–73Google Scholar
- 6.Kusy B, Ledeczi A, Koutsoukos X. Tracking mobile nodes using RF doppler shifts. In: Proc of ACM SenSys’07. Sydney, Australia, 2007. 29–42Google Scholar
- 7.Kusy B, Sallai J, Balogh G, et al. Radio interferometric tracking of mobile wireless nodes. In: Proc of MobiSys’07, San Juan, Puerto Rico, 2007. 139–151Google Scholar
- 9.Kusy B, Balogh G, Maroti M. In track: high precision tracking of mobile sensor nodes. In: Proceedings of 4th European Workshop on Wireless Sensor Networks (EWSN 2007). Berlin: Springer. 2007. 51–66Google Scholar
- 10.Wang X Z, Moran B, Brazil M. Hyperbolic positioning using RIPS measurements for wireless sensor networks. In: Proceedings of 15th IEEE International Conference on Networks (ICON’07). Adelaide, SA, Australia, 2007. 425–430Google Scholar
- 13.Xu B J, Li G, Huangfu K. Ambiguity problem of digitized multiple frequency CW ranging radar under noisy condition. Acta Electron Sin, 2002, 30: 903–906Google Scholar
- 14.Huang Z X, Wan Z. Range ambiguity resolution in multiple PRF pulse Doppler radars. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP’87). Dallas, TX, USA, 1987. 1786–1789Google Scholar