Gidugu Ananda Ramadass, Vandavasi Bala Naga Jyothi, Narayanaswamy Vedachalam
Ministry of Earth Sciences, National Institute of Ocean Technology, Chennai, India.
Sci Rep. 2024 Aug 2;14(1):17912. doi: 10.1038/s41598-024-68950-2.
The navigational accuracy of sea animals and trans-ocean birds provides inspiration in using geo-magnetic field (GMF) for realizing strategic truly autonomous underwater vehicles (AUV) capable of determining their absolute position on earth, without the aid of ship-referenced acoustic baseline systems. Supervised Machine Learning algorithms are applied on the GMF intensity data obtained from NOAA World Magnetic Model for a 900 km within the Indian mineral exploratory area in the Central Indian Ocean, with a resolution of 50 m, considering the sensitivity of commercially available magnetometers. It is identified that, for AUVs equipped with magnetometers with a detection sensitivity of 0.1 nT, the supervisory random forest regression and decision tree algorithm trained with priori GMF data, could provide trajectory guidance to AUVs with an absolute mean position accuracy in 2D plane, with reference to the last known position from Integrated Navigation system aided initially with GPS and with acoustic positioning in underwater. Circular Error Probable (CEP 50) of 53 m and 56 m, respectively. The scalar GMF anomaly navigation demonstrated to be a viable GPS-alternative navigation system could be extended to larger areas with inclination and declination vectors, as unique identifiers.
海洋动物和跨洋鸟类的导航精度为利用地磁场（GMF）实现战略性真正自主水下航行器（AUV）提供了灵感，这种水下航行器能够在不借助船舶参考声学基线系统的情况下确定其在地球上的绝对位置。考虑到市售磁力计的灵敏度，将监督机器学习算法应用于从美国国家海洋和大气管理局世界磁模型获得的、位于印度洋中部印度矿产勘探区域内900公里范围、分辨率为50米的地磁场强度数据。结果表明，对于配备检测灵敏度为0.1纳特斯拉磁力计的自主水下航行器，使用先验地磁场数据训练的监督随机森林回归和决策树算法，能够为自主水下航行器在二维平面内提供轨迹引导，相对于最初由全球定位系统辅助并在水下进行声学定位的组合导航系统的最后已知位置，其绝对平均位置精度的圆概率误差（CEP 50）分别为53米和56米。标量地磁场异常导航被证明是一种可行的全球定位系统替代导航系统，可利用倾角和偏角矢量作为唯一标识符扩展到更大区域。
