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8 June 2022 The Luenberger sliding innovation filter for linear systems
Mohammad AlShabi, Andrew Gadsden
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Proceedings Volume 12122, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXI; 121220B (2022) https://doi.org/10.1117/12.2619570
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States
Abstract
In this paper, the newly developed sliding innovation filter (SIF) is reformulated to accommodate the ability of extracting the hidden states. This is accomplished by using the well-known Luenberger technique, which is commonly used by observers. In this paper, the SIF is applied to a linear system, which has fewer measurements than states. The results show that the proposed filter extracts the hidden state with small RMSE, as low as 0.1, and small MAE, as low as 1.
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Mohammad AlShabi and Andrew Gadsden "The Luenberger sliding innovation filter for linear systems", Proc. SPIE 12122, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXI, 121220B (8 June 2022); https://doi.org/10.1117/12.2619570
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KEYWORDS
Filtering (signal processing)
Control systems
Sensors
Linear filtering
Matrices
Signal processing
Systems modeling
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