报告题目：A DYNAMIC BAYESIAN NONPARAMETRIC MODEL FOR BLIND CALIBRATION OF SENSOR NETWORKS

报 告 人：钟雄虎博士（新加坡南洋理工大学高级博士后研究员）

报告时间：2017年6月16日下午4:00-5:30

报告地点：航海学院东配楼

邀 请 人：姜喆副教授

报告摘要：

In the sensor network blind calibration problem, the gains and offsets of sensors are estimated from noisy observations of unknown underlying signals. This is in general a non-identifiable problem, unless restrictive assumptions on the signal subspace or sensor observations are imposed. To overcome these assumptions, we propose a dynamic Bayesian nonparametric model. We show that if the unknown underlying signals follow the first-order auto-regressive process, then the sensor gains and offsets are identifiable. Furthermore, our model allows sensors to form clusters, where each cluster observes the same underlying signal. The clusters are however not known a priori, and are learned through the sensor data. We present a block Gibbs sampling inference method based on the forward filtering backward sampling algorithm. Simulation results suggest that our approach can estimate the sensor gains and offsets with good accuracy, and performs better than methods that first perform clustering and then blind calibration.

报告人简介：

钟雄虎博士于2010年获得英国爱丁堡大学工程学院数字通信研究所博士学位，主要研究方向为非参数贝叶斯学习以及网络传感器信息融合。目前于新加坡南洋理工大学担任高级博士后研究员，从事智能车联网通信技术及大数据处理项目的研究工作。并分别于2013年和2014年应邀在德国波恩大学和英国萨里大学进行短期合作研究。共发表国际期刊及会议论文50余篇，并于2013年获得第12届海洋电子国际会议最佳论文奖，自2013年来一直担任IET Signal Processing期刊副主编。