本文考虑非高斯噪声下传感器网络的状态估计问题.在机动目标跟踪、室内定位、水声导航等应用中,传感器的非高斯噪声会造成针对高斯噪声设计的常规状态估计算法精度下降.在现有针对单传感器系统的基于多变量Laplace(Multivariate Laplace...本文考虑非高斯噪声下传感器网络的状态估计问题.在机动目标跟踪、室内定位、水声导航等应用中,传感器的非高斯噪声会造成针对高斯噪声设计的常规状态估计算法精度下降.在现有针对单传感器系统的基于多变量Laplace(Multivariate Laplace,ML)鲁棒状态估计(Robust State Estimation based on ML,RSE-ML)算法基础上,本文借助信息滤波的特点,推导了针对多传感器系统的集中式RSE-ML(Centralized RSE-ML,CRSE-ML)算法,进一步利用一致性平均得到分布式RSE-ML(Distributed RSE-ML,DRSE-ML)算法.本文提出的DRSE-ML算法中利用ML建模非高斯噪声,借助变分贝叶斯方法估计噪声和状态参数,采用一致性算法进行分布式信息交互,克服了集中式算法通信和计算负担重的缺点,且具有自由参数少、估计精度高的特点.仿真结果表明,所提出的DRSE-ML算法估计精度优于现有相关算法,且能逼近集中式CRSE-ML算法的估计精度.展开更多
In Wyner-Ziv (WZ) Distributed Video Coding (DVC), correlation noise model is often used to describe the error distribution between WZ frame and the side information. The accuracy of the model can influence the perform...In Wyner-Ziv (WZ) Distributed Video Coding (DVC), correlation noise model is often used to describe the error distribution between WZ frame and the side information. The accuracy of the model can influence the performance of the video coder directly. A mixture correlation noise model in Discrete Cosine Transform (DCT) domain for WZ video coding is established in this paper. Different correlation noise estimation method is used for direct current and alternating current coefficients. Parameter estimation method based on expectation maximization algorithm is used to estimate the Laplace distribution center of direct current frequency band and Mixture Laplace-Uniform Distribution Model (MLUDM) is established for alternating current coefficients. Experimental results suggest that the proposed mixture correlation noise model can describe the heavy tail and sudden change of the noise accurately at high rate and make significant improvement on the coding efficiency compared with the noise model presented by DIStributed COding for Video sERvices (DISCOVER).展开更多
文摘本文考虑非高斯噪声下传感器网络的状态估计问题.在机动目标跟踪、室内定位、水声导航等应用中,传感器的非高斯噪声会造成针对高斯噪声设计的常规状态估计算法精度下降.在现有针对单传感器系统的基于多变量Laplace(Multivariate Laplace,ML)鲁棒状态估计(Robust State Estimation based on ML,RSE-ML)算法基础上,本文借助信息滤波的特点,推导了针对多传感器系统的集中式RSE-ML(Centralized RSE-ML,CRSE-ML)算法,进一步利用一致性平均得到分布式RSE-ML(Distributed RSE-ML,DRSE-ML)算法.本文提出的DRSE-ML算法中利用ML建模非高斯噪声,借助变分贝叶斯方法估计噪声和状态参数,采用一致性算法进行分布式信息交互,克服了集中式算法通信和计算负担重的缺点,且具有自由参数少、估计精度高的特点.仿真结果表明,所提出的DRSE-ML算法估计精度优于现有相关算法,且能逼近集中式CRSE-ML算法的估计精度.
基金Supported by the National Natural Science Foundation of China (No. 61071091)Jiangsu Province Graduate Innovative Research Plan (CX07B_107Z)
文摘In Wyner-Ziv (WZ) Distributed Video Coding (DVC), correlation noise model is often used to describe the error distribution between WZ frame and the side information. The accuracy of the model can influence the performance of the video coder directly. A mixture correlation noise model in Discrete Cosine Transform (DCT) domain for WZ video coding is established in this paper. Different correlation noise estimation method is used for direct current and alternating current coefficients. Parameter estimation method based on expectation maximization algorithm is used to estimate the Laplace distribution center of direct current frequency band and Mixture Laplace-Uniform Distribution Model (MLUDM) is established for alternating current coefficients. Experimental results suggest that the proposed mixture correlation noise model can describe the heavy tail and sudden change of the noise accurately at high rate and make significant improvement on the coding efficiency compared with the noise model presented by DIStributed COding for Video sERvices (DISCOVER).