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5 August 2015 A novel track-before-detect algorithm based on optimal nonlinear filtering for detecting and tracking infrared dim target
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Abstract
Aiming at the nonlinear and non-Gaussian features of the real infrared scenes, an optimal nonlinear filtering based algorithm for the infrared dim target tracking-before-detecting application is proposed. It uses the nonlinear theory to construct the state and observation models and uses the spectral separation scheme based Wiener chaos expansion method to resolve the stochastic differential equation of the constructed models. In order to improve computation efficiency, the most time-consuming operations independent of observation data are processed on the fore observation stage. The other observation data related rapid computations are implemented subsequently. Simulation results show that the algorithm possesses excellent detection performance and is more suitable for real-time processing.
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Yuexin Tian, Kun Gao, Ying Liu, and Lu Han "A novel track-before-detect algorithm based on optimal nonlinear filtering for detecting and tracking infrared dim target ", Proc. SPIE 9622, 2015 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology, 96220U (5 August 2015); https://doi.org/10.1117/12.2189993
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