{"title":"Kalman filter based phase delay reduction technique","authors":"Shahjahan Shaik, J. Popat, T. Kishore Kumar","doi":"10.1109/ICRTIT.2016.7569549","DOIUrl":null,"url":null,"abstract":"Phase delay and phase noise are the serious problems in wireless communication, which may degrade the total system performance and brings instability. In satellite position controlling systems, accelerometer sends the position information to the radar through free space, and then forwards to control system on the ground. Received signal contains high frequency random noise along with satellite position information. By using the low pass filters like Butterworth, Chebechev, can remove the high frequency noise. These filters provide phase delays and may cause the change in total system phase margin, stability and accuracy of information present in the signal. This paper presents a Kalman filtering based approach to overcome the phase delay and stability problems which estimates the desired sensor signal by taking a noisy contaminated sensor signal as input.","PeriodicalId":351133,"journal":{"name":"2016 International Conference on Recent Trends in Information Technology (ICRTIT)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Conference on Recent Trends in Information Technology (ICRTIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICRTIT.2016.7569549","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Phase delay and phase noise are the serious problems in wireless communication, which may degrade the total system performance and brings instability. In satellite position controlling systems, accelerometer sends the position information to the radar through free space, and then forwards to control system on the ground. Received signal contains high frequency random noise along with satellite position information. By using the low pass filters like Butterworth, Chebechev, can remove the high frequency noise. These filters provide phase delays and may cause the change in total system phase margin, stability and accuracy of information present in the signal. This paper presents a Kalman filtering based approach to overcome the phase delay and stability problems which estimates the desired sensor signal by taking a noisy contaminated sensor signal as input.