NEURAL NETWORKS FOR MULTISENSOR DATA FUSION

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Authors
  1. Litva, J.
  2. Wang, F.
  3. Lo, T.
Corporate Authors
Defence Research Establishment Valcartier, Valcartier QUE (CAN);McMaster Univ, Hamilton ONT (CAN) Communications Research Lab
Abstract
With the recent proliferation and increasing sophistication of new technologies, it is recognized that the incorporation of new techniques, such as neural networks and others, will make the data fusion system more powerful in tri-service. The research activity presented in this reports has been carried out by Defence Research Establishment Valcartier, and Communications Research Laboratory, McMaster University to study the multisensor data fusion with artificial neural networks (ANN). Both multisensor data fusion for airborne target classification with ANN and neural data association are studied. A new technique of measurement data association in a multitarget radar environment is developed. The technique is based on the mean-field-theory machine and has the advantages of both the Hopfield network and the Boltzmann machine. In the technical development, three new energy functions have been created. Theoretically, the critical annealing temperature is found to determine the annealing temperature range. Neural data association capacities have been evaluated in the cases without clutter and with clutters for different measurement radar precisions. The new energy functions have been extended to multiple dimensional data association. A comprehensive analysis by computer simulations has demonstrated that the new technique developed in this report possesses high association capacity in the presence of false alarms, it can cope with track-crossing in
Keywords
Back propagation;Multidimensional data association;Track association;Hopfield Neural network;Mean-field-theory machine;MSDF (Multi-Sensor Data Fusion);MSDF (Multi-Source Data Fusion)
Report Number
DREV-CR-870;CRL-298 — Technical Report
Date of publication
01 Apr 1995
Number of Pages
70
DSTKIM No
96-02297
CANDIS No
155607
Format(s):
Document Image stored on Optical Disk;Hardcopy

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