A machine learning-based state estimation approach for varying noise distributions

Waleed Hilal; Stephen A. Gadsden; John Yawney

doi:10.1117/12.2663898

14 June 2023 A machine learning-based state estimation approach for varying noise distributions

Waleed Hilal, Stephen A. Gadsden, John Yawney

Proceedings Volume 12547, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXII; 1254706 (2023) https://doi.org/10.1117/12.2663898
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

The field of estimation theory is concerned with providing a system with the ability to extract relevant information about the environment, resulting in more effective interaction with the system’s surroundings through more well-informed, robust control actions. However, environments often exhibit high degrees of nonlinearity and other unwanted effects, posing a significant problem to popular techniques like the Kalman filter (KF), which yields an optimal only under specific conditions. One of these conditions is that the system and measurement noises are Gaussian, zero-mean with known covariance, a condition often hard to satisfy in practical applications. This research aims to address this issue by proposing a machine learning-based estimation approach capable of dealing with a wider range of noise types without the need for a known covariance. Harnessing the generative capabilities of machine learning techniques, we will demonstrate that the resultant model will prove to be a robust estimation strategy. Experimental simulations are carried out comparing the proposed approach with other conventional approaches on different varieties of functions corrupted by noises of varying distribution types.

Conference Presentation

Citation Download Citation

Waleed Hilal, Stephen A. Gadsden, and John Yawney "A machine learning-based state estimation approach for varying noise distributions", Proc. SPIE 12547, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXII, 1254706 (14 June 2023); https://doi.org/10.1117/12.2663898

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