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Noise Covariance Matrix Estimation in Target Tracking in Three Approaches: n-step Prediction, Kalman Gain Covariance and Gamma-distribution of Noise Statistics
Sahbasadat Rajamand, Abdulhamid Zahedi,
Volume 22, Issue 3 (9-2026)
Abstract
Noise parameters in many target tracking projects are assumed as known factors which is a main challenge because of uncertainty in measurement and state-model noise. Thus, many papers are focused on the accurate estimation of noise statistics. This paper is concentrated on this subject where it is tried to present three simple efficient methods in this regard. Estimation using n-step prediction, applying Kalman filter covariance and using Gamma distribution for noise parameters are the main concepts of the three proposed methods. Simulation results show the efficiency of all methods compared to other methods in the literature where the Gamma-distribution-based method is the most efficient work among other suggested ones in term of estimation error.
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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.