24 October 2017 Infrared/radar data fusion and tracking algorithm based on the multi-scale model
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Proceedings Volume 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications; 1046257 (2017) https://doi.org/10.1117/12.2285729
Event: Applied Optics and Photonics China (AOPC2017), 2017, Beijing, China
Abstract
Infrared and Radar data fusion algorithms have drawn a great deal of attention due to its implementation of complementary information, improvement of target tracking and enhancement of system viability. However, in the step of estimating the target state by multi-sensor, different sampling rates between two sensors make it difficult for data fusion. In order to solve this problem and make full use of the advantages of the data obtained by multi-sensor, an effective state estimation algorithm by combining the theory of multi-scale and converted measurement Kalman filter (CMKF) algorithm is presented in this paper. By establishing the multi-scale model, target state is estimated at the finest scale with the Interacting Multiple Model (IMM) algorithm at first. Then, at the coarse scale, appropriate observational information is selected in accordance with specific conditions. Angle information estimated by infrared sensor and the distance information obtained by radar sensor are fused to locate the target when two sensors have the same sampling time instant, otherwise, the target is located only by using the angle and distance information acquired by radar sensor. In addition, CMKF algorithm is used to estimate the target state and obtain the optimal fusion estimation. The simulation results under the environment of MATLAB show that the proposed algorithm effectively improves the precision and the instability of infrared/radar detection system.
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Yongli Sun, Yongli Sun, Bingjian Wang, Bingjian Wang, Xiang Yi, Xiang Yi, Ge Hu, Ge Hu, } "Infrared/radar data fusion and tracking algorithm based on the multi-scale model", Proc. SPIE 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications, 1046257 (24 October 2017); doi: 10.1117/12.2285729; https://doi.org/10.1117/12.2285729
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