The Luenberger sliding innovation filter for linear systems

Mohammad AlShabi; Andrew Gadsden

doi:10.1117/12.2619570

8 June 2022 The Luenberger sliding innovation filter for linear systems

Mohammad AlShabi, Andrew Gadsden

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.

Citation Download Citation

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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available