Position‐based adaptive quantization for target location estimation in wireless sensor networks using one‐bit data. Issue 8 (6th February 2015)

• Record Type: Journal Article
• Authors: Liu, Guiyun; Liu, Hua; Chen, Hongbin; Zhou, Chao; Shu, Lei
• Is Part Of: Wireless communications and mobile computing. Volume 16:Issue 8(2016)
• Page Start: 929
• Page End: 941
• Publication Date: 2015-02-06
• Subjects: wireless sensor networks -- distributed estimation -- quantization; Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1002/wcm.2576
• Languages: English
• ISSNs: 1530-8669

Abstract

Abstract: 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 Cram é r–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 Cram é r–Rao lower bound is less than its fixed‐quantization Cram é r‐Rao lower bound. Copyright © 2015 John Wiley & Sons, Ltd. Abstract : The problem of target location estimation in a wireless sensor network is considered, where each sensor only transmits one‐bit information to its fusion center. To improve the performance of estimation, a position‐based adaptive quantization scheme is proposed to make a good choice of quantizer' thresholds, and each sensor node dynamically adjusts its quantization threshold according to a kind of position‐based information sequences. Numerical results show that the position‐based maximum likelihood estimator is more accurate than the classical fixed‐quantization maximum likelihood estimator.