Cooperative localization based on severely quantized RSS measurements in wireless sensor network
2016 (English)In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016, 4214-4218 p.Conference paper (Refereed)
We study severely quantized received signal strength (RSS)-based cooperative localization in wireless sensor networks. We adopt the well-known sum-product algorithm over a wireless network (SPAWN) framework in our study. To address the challenge brought by severely quantized measurements, we adopt the principle of importance sampling and design appropriate proposal distributions. Moreover, we propose a parametric SPAWN in order to reduce both the communication overhead and the computational complexity. Experiments with real data corroborate that the proposed algorithms can achieve satisfactory localization accuracy for severely quantized RSS measurements. In particular, the proposed parametric SPAWN outperforms its competitors by far in terms of communication cost. We further demonstrate that knowledge about non-connected sensors can further improve the localization accuracy of the proposed algorithms.
Place, publisher, year, edition, pages
2016. 4214-4218 p.
Distributed cooperative localization, SPAWN, quantized RSS, wireless sensor network
IdentifiersURN: urn:nbn:se:liu:diva-128632DOI: 10.1109/ICASSP.2016.7472471OAI: oai:DiVA.org:liu-128632DiVA: diva2:930921
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China, 20-25 March, 2016