報(bào)告題目：A DYNAMIC BAYESIAN NONPARAMETRIC MODEL FOR BLIND CALIBRATION OF SENSOR NETWORKS
報(bào) 告 人：鐘雄虎博士（新加坡南洋理工大學(xué)高級(jí)博士后研究員）
報(bào)告時(shí)間：2017年6月16日下午4:00-5:30
報(bào)告地點(diǎn)：航海學(xué)院東配樓
邀 請(qǐng) 人：姜喆副教授
報(bào)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.
報(bào)告人簡(jiǎn)介：
鐘雄虎博士于2010年獲得英國(guó)愛(ài)丁堡大學(xué)工程學(xué)院數(shù)字通信研究所博士學(xué)位，主要研究方向?yàn)榉菂?shù)貝葉斯學(xué)習(xí)以及網(wǎng)絡(luò)傳感器信息融合。目前于新加坡南洋理工大學(xué)擔(dān)任高級(jí)博士后研究員，從事智能車(chē)聯(lián)網(wǎng)通信技術(shù)及大數(shù)據(jù)處理項(xiàng)目的研究工作。并分別于2013年和2014年應(yīng)邀在德國(guó)波恩大學(xué)和英國(guó)薩里大學(xué)進(jìn)行短期合作研究。共發(fā)表國(guó)際期刊及會(huì)議論文50余篇，并于2013年獲得第12屆海洋電子國(guó)際會(huì)議最佳論文獎(jiǎng)，自2013年來(lái)一直擔(dān)任IET Signal Processing期刊副主編。
