Position-based adaptive quantization for target location estimation in wireless sensor networks using one-bit data

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• 作者：Guiyun Liu ; Hua Liu ; Hongbin Chen ; Chao Zhou and Lei Shu
• 关键词：wireless sensor networks ; distributed estimation ; quantization
• 刊名：Wireless Communications and Mobile Computing
• 出版年：2016
• 出版时间：June 2016
• 年：2016
• 卷：16
• 期：8
• 页码：929-941
• 全文大小：1161K
• ISSN：1530-8677

文摘

The problem of target location estimation in a wireless sensor network is considered, where due to the bandwidth and power constraints, each sensor only transmits one-bit information to its fusion center. To improve the performance of estimation, a position-based adaptive quantization scheme for target location estimation in wireless sensor networks is proposed to make a good choice of quantizer' thresholds. By the proposed scheme, each sensor node dynamically adjusts its quantization threshold according to a kind of position-based information sequences and then sends its one-bit quantized version of the original observation to a fusion center. The signal intensity received at local sensors is modeled as an isotropic signal intensity attenuation model. The position-based maximum likelihood estimator as well as its corresponding position-based Cramér–Rao lower bound are derived. Numerical results show that the position-based maximum likelihood estimator is more accurate than the classical fixed-quantization maximum likelihood estimator and the position-based Cramér–Rao lower bound is less than its fixed-quantization Cramér-Rao lower bound. Copyright