NEW COMPUTATIONAL TECHNIQUE FOR COMPLEMENTARY SENSOR INTEGRATION IN DETECTION LOCALIZATION SYSTEMS

The integration of data obtained from several different sensors has of ten been proposed as a strategy by which accurate estimates of the val ues of physical variables being measured may be obtained when the sens or data are corrupted by noise. This paper considers a pair of detecti on-localization sensor systems that have capabilities complementing ea ch other. One has higher resolution than the other but is more suscept ible to non-Gaussian multiplicative and additive noise than the sensor with lower resolution. Both are subject to additive Gaussian white no ise. Studies have been made in the past to characterize such systems a nd to make accurate estimates of signals of interest, We propose an al ternative computational framework that makes fewer assumptions and the reby makes the system more realistic. The distinguishing feature of ou r method is that our solution involves only polynomial time and space complexity and hence is well suited for use in realtime applications. Extensive simulation results are included to prove the effectiveness o f our solution under varied random noise levels in the sensor data. (C ) 1996 Society of Photo-Optical instrumentation Engineers.