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21 January 2019 Diffusion-based cooperative space object tracking
Bin Jia, Khanh Pham, Erik Blasch, Genshe Chen, Dan Shen
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Optical Engineering, Vol. 58, Issue 4, 041607 (January 2019). https://doi.org/10.1117/1.OE.58.4.041607
Abstract
We propose the diffusion-based enhanced covariance intersection cooperative space object tracking (DeCiSpOT) filter. The main advantage of the proposed DeCiSpOT algorithm is that it can balance the computational complexity and communication requirements between different sensors as well as improve track accuracy when measurements do not exist or are of low accuracy. Instead of using the standard covariance intersection in the diffusion step, the enhanced diffusion strategy integrates the 0-1 weighting covariance intersection strategy and the iterative covariance intersection strategy. The proposed DeCiSpOT algorithm also uses the global nearest neighbor and probabilistic data association for multiple space object tracking. Two typical scenarios including cooperative single and multiple space object tracking are used to demonstrate the performance of the proposed DeCiSpOT filter. Using simulated ground-based electro-optical (EO) measurements for multiple resident space objects and multiple distributed EO sensors, the DeCiSpOT archived results comparable to an optimal centralized approach. The results demonstrate that the DeCiSpOT is effective for space object tracking problem with results close to the optimal centralized cubature Kalman filter.
© 2019 Society of Photo-Optical Instrumentation Engineers (SPIE) 0091-3286/2019/$25.00 © 2019 SPIE
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Bin Jia, Khanh Pham, Erik Blasch, Genshe Chen, and Dan Shen "Diffusion-based cooperative space object tracking," Optical Engineering 58(4), 041607 (21 January 2019). https://doi.org/10.1117/1.OE.58.4.041607
Received: 12 September 2018; Accepted: 18 December 2018; Published: 21 January 2019
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Cited by 9 scholarly publications.
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KEYWORDS
Sensors
Diffusion
Electronic filtering
Detection and tracking algorithms
Filtering (signal processing)
Optical engineering
Nonlinear filtering
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