24 November 2014 Asynchronous data fusion of infrared imaging and laser ranging for target tracking
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Proceedings Volume 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition; 93011F (2014) https://doi.org/10.1117/12.2072040
Event: International Symposium on Optoelectronic Technology and Application 2014, 2014, Beijing, China
Abstract
In a multi-sensor system, sensors often provide data at different rates with different communication delays. To the asynchronous fusion for target tracking with infrared and laser sensors, that provide range and bearing information respectively, there mainly exist two problems need to be solved. One is temporal registration of different sensors since infrared sensor has a shorter sample time than laser sensor. The measurements are dealt with least square method, which makes full use of the high frequency characteristic of infrared sensor. The other is the choice of filtering algorithm because of the correlation of coordinate transformation when measurement equation is described in Cartesian coordinate. Converted measurement Kalman filter (CMKF) is a popular solution to the nonlinear estimation problem expect nonlinear filtering algorithm. Simulations of the algorithms adopted in the paper were carried out to track the target and the performance of different algorithms was compared. The results show that the algorithm based on CMKF yields better track performance than the fusion algorithm based on standard Kalman filter (KF) and a little worse than the algorithm based on unscented Kalman filter (UKF).
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Jingya Hao, Jingya Hao, BingJian Wang, BingJian Wang, Jia Li, Jia Li, } "Asynchronous data fusion of infrared imaging and laser ranging for target tracking", Proc. SPIE 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition, 93011F (24 November 2014); doi: 10.1117/12.2072040; https://doi.org/10.1117/12.2072040
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