16 September 2005 Optimal multiple-lag out-of-sequence measurement algorithm based on generalized smoothing framework
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Abstract
Out-of-sequence measurement (OOSM) filtering algorithms have drawn a great deal of attention during the last few years. A number of multiple-lag OOSM filtering algorithms exists in the research literature. Only one of the OOSM filtering algorithms is optimal and remaining algorithms are suboptimal even for the linear dynamics and linear measurement models with additive Gaussian noises. A general feature of each OOSM filtering algorithm is that the algorithm calculates optimally or sub-optimally, the smoothed or retrodicted state estimate, associated covariance, and cross-covariance between the state and the measurement at the OOSM time. The existing optimal OOSM algorithm calculates these three quantities using a forward recursive algorithm. In this paper, we show that the OOSM filtering problem can be solved optimally using a generalized smoothing or retrodiction framework for the linear dynamics and linear measurement models with additive Gaussian noises. We develop a new optimal smoothing based OOSM filtering algorithm which uses the Rauch-Tung-Streibel (RTS) fixed-interval optimal backward smoother. We present numerical results using simulated data which includes two-dimensional position and velocity measurements and analyze the performance of the algorithm using Monte Carlo simulations.
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Mahendra Mallick, Keshu Zhang, "Optimal multiple-lag out-of-sequence measurement algorithm based on generalized smoothing framework", Proc. SPIE 5913, Signal and Data Processing of Small Targets 2005, 591308 (16 September 2005); doi: 10.1117/12.624555; https://doi.org/10.1117/12.624555
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