Volume 22, Issue 3 (September 2026)                   IJEEE 2026, 22(3): 3685-3685 | Back to browse issues page

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Rajamand S, Zahedi A. Noise Covariance Matrix Estimation in Target Tracking in Three Approaches: n-step Prediction, Kalman Gain Covariance and Gamma-distribution of Noise Statistics. IJEEE 2026; 22 (3) :3685-3685
URL: http://ijeee.iust.ac.ir/article-1-3685-en.html
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
Abstract:   (125 Views)
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.
Keywords: Noise Covariance Matrix Estimation, Target Tracking, n-step Prediction, Kalman Gain Covariance, Gamma-distribution.
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Type of Study: Research Paper | Subject: Radar and Sonar
Received: 2025/01/07 | Revised: 2026/05/04 | Accepted: 2025/12/16
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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.