Paper
8 June 2022 The Luenberger sliding innovation filter for linear systems
Author Affiliations +
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.
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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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