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用于估计无人水面舰艇(USV)对地航速(SOG)、对地航向(COG)和航向变化率的五状态扩展卡尔曼滤波器:实验结果

Five-State Extended Kalman Filter for Estimation of Speed over Ground (SOG), Course over Ground (COG) and Course Rate of Unmanned Surface Vehicles (USVs): Experimental Results.

作者信息

Fossen Sindre, Fossen Thor I

机构信息

Maritime Robotics AS, Brattørkaia 11, Pirterminalen, 7010 Trondheim, Norway.

Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway.

出版信息

Sensors (Basel). 2021 Nov 27;21(23):7910. doi: 10.3390/s21237910.

Abstract

Small USVs are usually equipped with a low-cost navigation sensor suite consisting of a global navigation satellite system (GNSS) receiver and a magnetic compass. Unfortunately, the magnetic compass is highly susceptible to electromagnetic disturbances. Hence, it should not be used in safety-critical autopilot systems. A gyrocompass, however, is highly reliable, but it is too expensive for most USV systems. It is tempting to compute the heading angle by using two GNSS antennas on the same receiver. Unfortunately, for small USV systems, the distance between the antennas is very small, requiring that an RTK GNSS receiver is used. The drawback of the RTK solution is that it suffers from dropouts due to ionospheric disturbances, multipath, interference, etc. For safety-critical applications, a more robust approach is to estimate the course angle to avoid using the heading angle during path following. The main result of this article is a five-state extended Kalman filter (EKF) aided by GNSS latitude-longitude measurements for estimation of the course over ground (COG), speed over ground (SOG), and course rate. These are the primary signals needed to implement a course autopilot system onboard a USV. The proposed algorithm is computationally efficient and easy to implement since only four EKF covariance parameters must be specified. The parameters need to be calibrated for different GNSS receivers and vehicle types, but they are not sensitive to the working conditions. Another advantage of the EKF is that the autopilot does not need to use the COG and SOG measurements from the GNSS receiver, which have varying quality and reliability. It is also straightforward to add complementary sensors such as a Doppler Velocity Log (DVL) to the EKF to improve the performance further. Finally, the performance of the five-state EKF is demonstrated by experimental testing of two commercial USV systems.

摘要

小型无人水面艇通常配备一套低成本导航传感器套件,该套件由全球导航卫星系统(GNSS)接收器和磁罗盘组成。不幸的是,磁罗盘极易受到电磁干扰。因此,它不应被用于对安全至关重要的自动驾驶系统。然而,陀螺罗盘非常可靠,但对于大多数无人水面艇系统来说成本过高。利用同一接收器上的两个GNSS天线来计算航向角很有吸引力。不幸的是,对于小型无人水面艇系统,天线之间的距离非常小,这就要求使用实时动态(RTK)GNSS接收器。RTK解决方案的缺点是,它会因电离层干扰、多径、干扰等因素而出现信号丢失。对于对安全至关重要的应用,一种更稳健的方法是估计航向角,以避免在路径跟踪过程中使用航向角。本文的主要成果是一种五状态扩展卡尔曼滤波器(EKF),它借助GNSS经纬度测量来估计对地航向(COG)、对地速度(SOG)和航向变化率。这些是在无人水面艇上实现航向自动驾驶系统所需的主要信号。所提出的算法计算效率高且易于实现,因为只需指定四个EKF协方差参数。这些参数需要针对不同的GNSS接收器和艇型进行校准,但它们对工作条件不敏感。EKF的另一个优点是自动驾驶仪无需使用来自GNSS接收器的COG和SOG测量值,这些测量值的质量和可靠性各不相同。将诸如多普勒速度计(DVL)等互补传感器添加到EKF中以进一步提高性能也很简单。最后,通过对两个商用无人水面艇系统进行实验测试,验证了五状态EKF的性能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20db/8659471/5af821c21d4c/sensors-21-07910-g001.jpg

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