Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/212915
Title: Synergy of Out of Sequence Measurement and Missing Data in Wireless Sensor Network
Researcher: N. Shivashankarappa
Guide(s): Raol R. Jitendra
Keywords: Kalman filter and other algorithms, Lyapunov energy functional
Sensor Networks, Data transmission delays,
University: Jain University
Completed Date: 30/07/2018
Abstract: In certain engineering situations, and especially in wireless sensor networks, there are events wherein some delays occur in data transmission and at times some measurements newlinemight be randomly missing. Processing such data for tracking and prediction of targets of interest would cause inaccuracies. Kalman filter or its equivalent algorithms are mainly newlineused for estimation of the states of the dynamic systems. In this thesis the problem of newlinemodelling and filtering of such delayed states and missing data is handled in a synergistic manner; mainly using Kalman filter-like optimal filtering algorithms in the measurement newlinedata level fusion process. This combined approach of filtering of delayed states, and using randomly missing observations; and their further utilization in data fusion scheme is relatively novel. The sensor data fusion is becoming increasingly matured soft technology newlineand has found numerous applications in science and engineering and in image fusion also. Performance evaluation of the combined approach is done using numerical simulations.Four alternative algorithms are studied and their modifications to include the state delay and randomly missing measurements are presented. Especially:i) the gain fusion, ii) Hinfinity a posteriori filter, iii) H-infinity risk sensitive filter, and iv) H-infinity global filtering algorithms are modified and evaluated for sensor data fusion scenario using numerical simulations carried out in MATLAB.Also, a nonlinear observer based on the gain of the continuous time data fusion filter is presented, and asymptotic convergence result is derived using Lyapunov energy functional.Subsequently, nonlinear observers using a) Kalman gain, and b) H-infinity gain are presented for handling state delays, and missing measurement data.Thus, the filtering schemes and the observers proposed in the present thesis provide a definite step towards improvement for handling newlinestate delays and randomly missing data synergistically for wireless sensor networks. newline
Pagination: 114 p.
URI: http://hdl.handle.net/10603/212915
Appears in Departments:Department of Electronics Engineering

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01.title page.pdfAttached File47.04 kBAdobe PDFView/Open
02.declaration.pdf26.09 kBAdobe PDFView/Open
03.certificate.pdf31.57 kBAdobe PDFView/Open
04.abstract.pdf28.74 kBAdobe PDFView/Open
05.acknowledgement.pdf30.72 kBAdobe PDFView/Open
06.table of contents.pdf36.42 kBAdobe PDFView/Open
07.list of figures, tables and abbreviations.pdf41.13 kBAdobe PDFView/Open
08.chapter 1.pdf164.87 kBAdobe PDFView/Open
09.chapter 2.pdf322.95 kBAdobe PDFView/Open
10.chapter 3.pdf283.65 kBAdobe PDFView/Open
11.chapter 4.pdf346.02 kBAdobe PDFView/Open
12.chapter 5.pdf292.39 kBAdobe PDFView/Open
13.chapter 6.pdf158.39 kBAdobe PDFView/Open
14.references.pdf156.24 kBAdobe PDFView/Open


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