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具有大失准角的 UUV 的极横向初始对准算法。

Polar Transversal Initial Alignment Algorithm for UUV with a Large Misalignment Angle.

机构信息

College of Automation, Harbin Engineering University, Harbin 150001, China.

Beijing Mechanical & Electrical Overall Design Department, The Fourth Academy of China Aerospace Science & Industry Corporation (CASIC), Beijing 100854, China.

出版信息

Sensors (Basel). 2018 Sep 25;18(10):3231. doi: 10.3390/s18103231.

Abstract

The conventional initial alignment algorithms are invalid in the polar region. This is caused by the rapid convergence of the Earth meridians in the high-latitude areas. However, the initial alignment algorithms are important for the accurate navigation of Unmanned Underwater Vehicles. The polar transversal initial alignment algorithm is proposed to overcome this problem. In the polar transversal initial alignment algorithm, the transversal geographic frame is chosen as the navigation frame. The polar region in the conventional frames is equivalent to the equatorial region in the transversal frames. Therefore, the polar transversal initial can be effectively applied in the polar region. According to the complex environment in the polar region, a large misalignment angle is considered in this paper. Based on the large misalignment angle condition, the non-linear dynamics models are established. In addition, the simplified unscented Kalman filter (UKF) is chosen to realize the data fusion. Two comparison simulations and an experiment are performed to verify the performance of the proposed algorithm. The simulation and experiment results indicate the validity of the proposed algorithm, especially when large misalignment angles occur.

摘要

传统的初始对准算法在极地区域无效。这是由于地球子午线在高纬度地区迅速收敛造成的。然而,初始对准算法对于无人水下航行器的精确导航非常重要。提出了极地横向初始对准算法来克服这个问题。在极地横向初始对准算法中,选择横向地理框架作为导航框架。传统框架中的极地区域相当于横向框架中的赤道区域。因此,极地横向初始对准算法可以有效地应用于极地区域。根据极地复杂的环境,本文考虑了较大的失准角。基于大失准角条件,建立了非线性动力学模型。此外,选择简化的无迹卡尔曼滤波(UKF)来实现数据融合。进行了两个对比仿真和一个实验来验证所提出算法的性能。仿真和实验结果表明了该算法的有效性,特别是在大失准角的情况下。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/31f7/6210246/ed868dd91824/sensors-18-03231-g001.jpg

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