Tracked targets often exhibit common behaviors due to influences from the surrounding environment, such as wind or obstacles, which are usually modeled as noise. Here, these influences are modeled using sparse Gaussian processes that are learned online together with the state inference using an extended Kalman filter. The method can also be applied to time-varying influences and identify simple dynamic systems. The method is evaluated with promising results in a simulation and a real-world application.
Learning Target Dynamics While Tracking Using Gaussian Processes
IEEE Transactions on Aerospace and Electronic Systems ; 56 , 4 ; 2591-2602
2020-08-01
2226425 byte
Article (Journal)
Electronic Resource
English
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