首页|Researcher from Tsinghua University Details Findings in Machine Learning (A Nove l Method for Damping State Switching Based on Machine Learning of a Strapdown In ertial Navigation System)
Researcher from Tsinghua University Details Findings in Machine Learning (A Nove l Method for Damping State Switching Based on Machine Learning of a Strapdown In ertial Navigation System)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on artificial intelligence are presented in a new report. According to news originating from Beijing, Peopl e’s Republic of China, by NewsRx editors, the research stated, “The integrated n avigation system based on the Global Navigation Satellite System (GNSS) in conju nction with the strapdown inertial navigation system (SINS) and the Doppler Velo city Logger (DVL) is essential for accurate and long-distance navigation in mari time environments.” Financial supporters for this research include Basic Science Center Program of T he National Natural Science Foundation of China; China Postdoctoral Science Foun dation. The news reporters obtained a quote from the research from Tsinghua University: “However, the error of the integrated navigation system gradually diverges due t o the inevitable velocity measurement error of DVL when GNSS outages occur. To e nsure the high navigational accuracy and stability of SINS, it is necessary to d ynamically adjust the damping state of SINS provided externally. In this paper, we have developed a novel method for damping state switching based on machine le arning with SINS. We construct a model of the change in reference velocity error and use sliding window technology to obtain the reference velocity error for mo del training. Before training, the digital compass loop is designed to process a nd highlight the change in reference velocity change errors. In order to reduce the impact of the damping switching, a variable damping system is used to transf orm the traditional one-time switching of the damping coefficient into a gradual switching, effectively reducing the impact of a sudden change in the damping co efficient on the system.”
Tsinghua UniversityBeijingPeople’s R epublic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
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