1 March 1996 New computational technique for complementary sensor integration in detection-localization systems
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The integration of data obtained from several different sensors has often been proposed as a strategy by which accurate estimates of the values of physical variables being measured may be obtained when the sensor data are corrupted by noise. This paper considers a pair of detection-localization sensor systems that have capabilities complementing each other. One has higher resolution than the other but is more susceptible to non-Gaussian multiplicative and additive noise than the sensor with lower resolution. Both are subject to additive Gaussian white noise. Studies have been made in the past to characterize such systems and to make accurate estimates of signals of interest. We propose an alternative computational framework that makes fewer assumptions and thereby makes the system more realistic. The distinguishing feature of our 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 of our solution under varied random noise levels in the sensor data.
J. R. Maheshkumar, J. R. Maheshkumar, Vijay Veeranna, Vijay Veeranna, S. Sitharama Iyengar, S. Sitharama Iyengar, Richard Ree Brooks, Richard Ree Brooks, } "New computational technique for complementary sensor integration in detection-localization systems," Optical Engineering 35(3), (1 March 1996). https://doi.org/10.1117/1.600659 . Submission:

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