A detection probability localization algorithm of Wireless Sensor Networks based on distributed compressed-sensing

作者：Gao Zhijun^*; Wang Hongyu; Xue Chunyan; Han Zhonghua

来源：Journal of Information and Computational Science, 2014, 11(8): 2783-2791.

DOI：10.12733/jics20103582

• Cooperative localization
• Detection probabilities
• Development of science and technologies
• DV-hop
• Joint probability density function
• Localization
• Nyquist sampling theorem
• Self-localization methods

With fast development of science and technology, People gradually need more and more information, causing significant pressure on the sampling. The sampling rate must be two times higher than the highest frequency of the signal based on Nyquist sampling theorem. Compressed Sensing (CS) employs a special sampling method which can capture and represent compressible signals at a rate significantly below the Nyquist rate. It can relieve the pressure of sampling process in Wireless Sensor Networks. And a cooperative self-localization method based on probability for wireless sensor networks is proposed. The method firstly estimates the initial Position of the located node based on the joint Probability density function of the distance between the located node and the connected reference nodes. Further, based on the refinement principle, a cooperative localization method is studied by making the best of the neighbour nodes and giving the neighbour nodes some confidence. The method improves the estimation accuracy as well as makes more unknown nodes to be located.

• 出版日期2014