{"title":"基于极大似然估计方法的无线传感器网络信道参数和距离估计","authors":"Kaiyisah Hanis Mohd Azmi, S. Berber, M. Neve","doi":"10.1109/ICCCE.2016.91","DOIUrl":null,"url":null,"abstract":"This paper presents the theoretical approach in developing the estimator equations for estimating distance and unknown channel parameters of indoor environment based on the received signal strength (RSS) method in Wireless Sensor Networks. The estimator equations are derived by manipulating the lognormal propagation model via maximum likelihood estimation method. In this paper, the path loss exponent and variability of fading parameters in indoor environment are assumed to be unknown. The performance of the estimators in predicting the path loss exponent, the variability of fading components and most importantly, the distance between the receiver and transmitter are analysed through simulations and the data obtained from RSS measurement in three indoor environments (an ideal and two natural fading environments). The simulation and measurement results show that the accuracy and precision of the estimators are highly dependent on the level of fading present in an environment, with higher accuracy in the estimators' performances found in fading environment with a low variability.","PeriodicalId":360454,"journal":{"name":"2016 International Conference on Computer and Communication Engineering (ICCCE)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Channel Parameters and Distance Estimation in Wireless Sensor Networks Based on Maximum Likelihood Estimation Method\",\"authors\":\"Kaiyisah Hanis Mohd Azmi, S. Berber, M. Neve\",\"doi\":\"10.1109/ICCCE.2016.91\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents the theoretical approach in developing the estimator equations for estimating distance and unknown channel parameters of indoor environment based on the received signal strength (RSS) method in Wireless Sensor Networks. The estimator equations are derived by manipulating the lognormal propagation model via maximum likelihood estimation method. In this paper, the path loss exponent and variability of fading parameters in indoor environment are assumed to be unknown. The performance of the estimators in predicting the path loss exponent, the variability of fading components and most importantly, the distance between the receiver and transmitter are analysed through simulations and the data obtained from RSS measurement in three indoor environments (an ideal and two natural fading environments). The simulation and measurement results show that the accuracy and precision of the estimators are highly dependent on the level of fading present in an environment, with higher accuracy in the estimators' performances found in fading environment with a low variability.\",\"PeriodicalId\":360454,\"journal\":{\"name\":\"2016 International Conference on Computer and Communication Engineering (ICCCE)\",\"volume\":\"42 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 International Conference on Computer and Communication Engineering (ICCCE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCCE.2016.91\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Conference on Computer and Communication Engineering (ICCCE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCCE.2016.91","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Channel Parameters and Distance Estimation in Wireless Sensor Networks Based on Maximum Likelihood Estimation Method
This paper presents the theoretical approach in developing the estimator equations for estimating distance and unknown channel parameters of indoor environment based on the received signal strength (RSS) method in Wireless Sensor Networks. The estimator equations are derived by manipulating the lognormal propagation model via maximum likelihood estimation method. In this paper, the path loss exponent and variability of fading parameters in indoor environment are assumed to be unknown. The performance of the estimators in predicting the path loss exponent, the variability of fading components and most importantly, the distance between the receiver and transmitter are analysed through simulations and the data obtained from RSS measurement in three indoor environments (an ideal and two natural fading environments). The simulation and measurement results show that the accuracy and precision of the estimators are highly dependent on the level of fading present in an environment, with higher accuracy in the estimators' performances found in fading environment with a low variability.