15 April 2010 Spline filter for target tracking
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In this paper an efficient approach to nonlinear non-Gaussian state estimation based on spline filtering is presented. The estimation of the conditional probability density of the unknown state can be ideally achieved through Bayes rule. However, the associated computational requirements make it impossible to implement this online filter in practice. In the general particle filtering problem, estimation accuracy increases with the number of particles at the expense of increased computational load. In this paper, B-Spline interpolation is used to represent the density of the state pdf through a low order continuous polynomial. The motivation is to reduce the computational cost. The motion of spline control points and corresponding coefficients is achieved through implementation of the Fokker-Planck equation, which describes the propagation of state probability density function between measurement instants. This filter is applicable for a general state estimation problem as no assumptions are made about the underlying probability density.
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Donna L. Kocherry, R. Tharmarasa, Tom Lang, and T. Kirubarajan "Spline filter for target tracking", Proc. SPIE 7698, Signal and Data Processing of Small Targets 2010, 76980H (15 April 2010); doi: 10.1117/12.851075; https://doi.org/10.1117/12.851075

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