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随机性参数分布式量化估计及其最优比特分配
引用本文:沈志萍,陈军勇,邬依林. 随机性参数分布式量化估计及其最优比特分配[J]. 控制理论与应用, 2016, 33(8): 1074-1080
作者姓名:沈志萍  陈军勇  邬依林
作者单位:河南师范大学数学与信息科学学院,中航工业通飞研究院,广东第二师范学院计算机科学系
基金项目:国家自然科学基金项目(61273109, 60774057), 广东第二师范学院教授博士科研专项经费(2014ARF25), 广东省科技计划项目(2014A090906010, 2016A010106007), 河南师范大学博士科研启动经费(5101019170158), 河南省高等学校重点科研项目(16A120005)资助.
摘    要:本文研究总比特率给定下随机向量参数分布式量化估计及其最优比特分配问题.与现有文献大都假定每个传感器的量化比特率给定而不是最优分配下研究随机性参数的分布式量化估计问题不同的是,本文将综合考虑最优量化器、最优估计器算法以及给定总比特率下的最优比特分配问题.针对向量状态标量观测模型,首先借助现有文献给出基于量化观测的最优估计器及其误差协方差阵形式表达,其次得到各传感器的渐近最优量化器实际为著名的Lloyd-max量化器,且各传感器的渐近最优量化级数与信噪比成正比,同时引入一种次优的求解非负整数比特率的方法.考虑到当传感器数目比较大时,初始的最优估计器算法运算量很大,设计了一种渐近等价的迭代量化估计器算法,其计算负担大大减轻,且对于存在延迟或丢包的网络环境亦适用,增强了算法的鲁棒性.仿真结果表明,本文提出的最优比特分配方案估计性能明显优于一般的均匀比特分配方案.

关 键 词:最优比特分配   量化信号   最优设计   分布式算子   分布式量化估计   Lloyd-max量化器   最小均方误差
收稿时间:2015-06-01
修稿时间:2016-05-16

Distributed quantization estimation and optimal bit allocation for a random variable
SHEN Zhi-ping,CHEN Jun-yong and WU Yi-lin. Distributed quantization estimation and optimal bit allocation for a random variable[J]. Control Theory & Applications, 2016, 33(8): 1074-1080
Authors:SHEN Zhi-ping  CHEN Jun-yong  WU Yi-lin
Affiliation:College of Mathematics and Information Science, Henan Normal University,China Aviation Industry General Aircraft Research Institute,Department of Computer Science, Guangdong University of Education
Abstract:This paper studies distributed quantization estimation and optimal bit allocation problems of a random vectorparameter given a total bit rate. Different from the existing literature generally assumed that each sensor quantization bitrate is given rather than optimal bit allocation in researching the corresponding problem, this paper will combine the designof the optimal quantizer, the optimal estimator algorithm and the optimal bit allocation problem under a given total bit. Fora vector state scalar observation of an observation model, we first give the optimal estimator and its error covariance matrixin form based on the quantitative observation with the existing literature, and then to get a conclusion that the asymptoticoptimal quantizer of each sensor is actual the famous Lloyd-max quantizer, and that the asymptotic optimal quantitativelevel of each sensor is proportional to the signal-to-noise ratio (SNR), at the same time, we introduce a suboptimal methodof solving the non-negative integer bit rate. Considering when the number of sensors is larger, the original optimal estimatoralgorithm computational complexity is very big, we design a asymptotic equivalence iterative quantization estimatoralgorithm, which can greatly reduce the calculation burden, and can apply to the network environment with some delay orpacket loss, so this method can also enhance the robustness of the algorithm. Simulation results show that our designedmethod can achieve a significant amount of the estimation MSE reduction when compared with the uniform allocationscheme in which each sensor quantizes its observation with the identical bit.
Keywords:optimal bit allocation   quantization signal   optimal design   distributed algorithms   distributed quantization estimation   Lloyd-max quantization   minimum mean-square error
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