Investigation of Sensor Data Fusions using Continuous-Time Decentralized Kalman Filters (DKF)

Abstract

The integration of sensors deals with the accumulation of information from two or more sensors in network. However
the accumulation of data is typically the subject of research in the area of image analysis. Generally the reliability of
collected data might induce the problem of reconstructing to get its original form. The Various fusion technique has
already been implement for text data, image data and video data. The general exploration in the sensor data fusion is
to understand the behavioral pattern reformation of real time capturing with sense of accuracy. This also handle out
the problems of localizing, navigation and tracing problem. This paper is to study of Kalman Filters technique with
their key feature of State Vector Fusion, Measurement Fusion, Gain Fusion. The mathematical formulation for the
data fusion of sensors derived through the State Vector Fusion (SVF) and covariance time propagations. The present
investigation is to analysis of process of dynamically maintaining a model of the local external environment and deep
exploration of fusion technique using time decentralized Kalman Filters (DKF). Fusion of perceptual information is
at the heart of data fusion process.