Synergy of Out of Sequence Measurement and Missing Data in Wireless Sensor Network

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