Robust AUV navigation with non-Gaussian noise: Enhanced UKF with Maximum Correntropy and M-estimation methods

IF 4.3 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems Pub Date : 2025-04-15 DOI:10.1016/j.robot.2025.105007

Xiaona Sun , PinChi Li , Jiaming Zhang , Ziyun Chen , Bo He

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引用次数: 0

Abstract

Unscented Kalman Filter (UKF) is widely used in autonomous underwater vehicle (AUV) underwater navigation. The minimum mean square error (MMSE) criterion is the main optimization metric of UKF, which exhibits excellent performance under Gaussian noise assumption. However, the performance degrades drastically when the Doppler Velocity Log (DVL) measurement system is characterized by heavy-tailed noise. For this reason, this paper proposes two Student’s T-distribution kernel-based Maximum Correntropy Unscented Kalman variants, referred to as MCSTUKF and STUKF, respectively, which are processed using both linear and nonlinear regression models. Despite the integration of higher-order statistics in the Maximum Correntropy Criterion (MCC), residual errors caused by extremely large outliers in the DVL measurements can still exist, so this paper couples four M-estimation methods to further improve the performance of the variants by adjusting the noise weights and truncating the measurements. Notably, these innovative UKF filters are real-time in nature, validated by simulations and Sailfish 260 AUV sea trial date.

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非高斯噪声下的鲁棒AUV导航：基于最大相关熵和m估计方法的增强UKF

无气味卡尔曼滤波器（UKF）广泛应用于自主水下航行器（AUV）的水下导航。最小均方误差（MMSE）准则是UKF的主要优化指标，在高斯噪声假设下表现出优异的性能。然而，当多普勒速度测井（DVL）测量系统存在重尾噪声时，其性能会急剧下降。因此，本文提出了两个基于Student 's t分布核的最大相关熵无气味卡尔曼变量，分别称为MCSTUKF和STUKF，分别使用线性和非线性回归模型进行处理。尽管在最大相关系数准则（MCC）中集成了高阶统计量，但DVL测量中由于异常值过大而导致的残差仍然存在，因此本文将四种m估计方法耦合在一起，通过调整噪声权值和截断测量值来进一步提高变量的性能。值得注意的是，这些创新的UKF滤波器本质上是实时的，通过模拟和Sailfish 260 AUV海上试验数据进行了验证。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Robotics and Autonomous Systems 工程技术-机器人学

CiteScore

9.00

自引率

7.00%

发文量

164

审稿时长

4.5 months

期刊介绍： Robotics and Autonomous Systems will carry articles describing fundamental developments in the field of robotics, with special emphasis on autonomous systems. An important goal of this journal is to extend the state of the art in both symbolic and sensory based robot control and learning in the context of autonomous systems. Robotics and Autonomous Systems will carry articles on the theoretical, computational and experimental aspects of autonomous systems, or modules of such systems.