OPTIMUM SENSOR FUSION AND DATA COMPRESSION

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Authors
  1. Jin, Q.
  2. Li, J.Y.
  3. Luo, Z.Q.
  4. Wong, K.M.
  5. Yip, P.
Corporate Authors
Defence Research Establishment Valcartier, Valcartier QUE (CAN);McMaster Univ, Hamilton ONT (CAN) Communications Research Lab
Abstract
To facilitate and to take full advantage of sensor fusion, it is necessary to provide the system operator with a way to integrate the data so as to increase the reliability of detecting the presence of targets, of determining their identities and intents, and to improve the accuracy of estimating their locations, velocities, etc. The necessity to employ sensor fusion is becoming even more urgent recently due to the political and economic climate such that the down sizing of the military requires existing systems to become more effective with the minimum of up-grades. This implies the need to find new ways to share and merge data obtained from the various sensors. In general, sensors can be looked upon as windows into the physical environment in which the phenomenon under observation is taking place. Since the data collected by each sensor represent only partial information of the phenomenon under observation, a process of sensor data fusion is needed so that more complete information can be acquired. At the very basic level, data fusion includes detection, registration, association, correlation, filtering or tracking, and identification. In this report, the concentration is on the problem of target tracking, and more specifically on the tracking of targets using centralized Kalman filtering.
Keywords
Wavelets;Fractals;Sub-band filtering
Report Number
DREV-CR-869;CRL-296 — Technical Report
Date of publication
01 Mar 1995
Number of Pages
85
DSTKIM No
96-02284
CANDIS No
155606
Format(s):
Document Image stored on Optical Disk;Hardcopy

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