电子学报 ›› 2016, Vol. 44 ›› Issue (11): 2773-2779.DOI: 10.3969/j.issn.0372-2112.2016.11.029

• 学术论文 • 上一篇    下一篇

无线传感器网络中基于多比特量化的极大似然分布式估计方法

郭黎利, 高飞, 孙志国   

  1. 哈尔滨工程大学信息与通信工程学院, 黑龙江哈尔滨 150001
  • 收稿日期:2015-04-22 修回日期:2015-10-30 出版日期:2016-11-25
    • 通讯作者:
    • 高飞
    • 作者简介:
    • 郭黎利,男,1955年4月生于黑龙江哈尔滨.现为哈尔滨工程大学信息与通信工程学院教授.从事现代通信系统理论与技术、通信信号处理技术方面的研究;孙志国,男,1977年8月生于黑龙江哈尔滨.现为哈尔滨工程大学信息与通信工程学院副教授.从事数字通信、宽带信号检测方面的研究.
    • 基金资助:
    • 国家自然科学基金 (No.61271263,No.61101141)

Multi-level Quantization Scheme for Distributed Maximum Likelihood Estimation in Wireless Sensor Networks

GUO Li-li, GAO Fei, SUN Zhi-guo   

  1. College of Information and Communication Engineering, Harbin Engineering University. Harbin, Heilongjiang 150001, China
  • Received:2015-04-22 Revised:2015-10-30 Online:2016-11-25 Published:2016-11-25

摘要:

在无线传感器网络背景下的分布式估计中,由于传输网络对发送功率和传输带宽的限制,压缩信源冗余、降低通信数据量便成为一个重要的课题.为此,本文提出了一种基于多比特量化观测的分布式估计方法(MQS),利用渐进性能作为优化准则构造量化阈值优化问题,运用粒子群算法对其进行求解得到最优量化阈值,给出了克拉美罗下界的解析表达式,并与均匀量化方法(UQS)和未量化方法(NQS)进行对比.理论分析和仿真实验表明,MQS的性能优于UQS.当量化深度增大到3时,MQS的估计性能十分接近NQS的估计性能.

关键词: 无线传感器网络, 多比特量化, 分布式估计, 粒子群算法

Abstract:

In the context of distributed estimation in wireless sensor networks (WSN),due to transmission power/bandwidth constrains,it is significant to reduce size of transmitted data.In this paper,a distributed estimation scheme,namely,multi-level quantization scheme (MQS) is proposed.The quantization threshold optimization problem is formulated by using asymptotic performance as an optimality criterion.The optimum quantization thresholds are obtained by resorting to particle swarm optimization algorithm.The explicit expression of the Cramér-Rao lower bound is derived.The proposed method is compared with uniform quantization scheme (UQS) and no quantization scheme (NQS).Theoretical analysis and simulation results demonstrate that the MQS scheme outperforms the UQS.Moreover,with 3-bit quantization,the MQS can provide estimation performance very close to that of the NQS.

Key words: wireless sensor networks, multi-level quantization, distributed estimation, particle swarm optimization algorithm

中图分类号: