RUDN scientists have developed a new method for determining the orientation of objects in the antenna complex based on measurements of a strapdown inertial system
It is established that modern methods for determining orientation using measurements of satellite navigation systems or inertial orientation systems do not provide the required accuracy for solving the problem of orientation of large or medium-sized objects. In this regard, a new dynamic algorithm for estimating stochastic orientation parameters has been developed that is invariant to the nature of the base movement and provides stability and the required accuracy of estimation under the most General assumptions about the nature of interference of sensitive elements of a strapdown inertial orientation system built on the basis of WSG. The Rodriguez-Hamilton parameter vector is used as the observed vector of orientation parameters, and the accelerometer output signal is proposed to be used as its observer. Based on the stochastic nonlinear equations of the vector of parameters of the current orientation and the equations of stochastic models of accelerometer output signals constructed for the most General case of object motion, a generalized Kalman filter was formed that provides a General solution to the problem of estimating the orientation parameters of an arbitrary structure on a movable base. The results of numerical simulation confirmed the possibility of using the developed method for solving the problem of high-precision orientation determination without correction from global satellite navigation systems over a long time interval.
An article in the journal Measurement techniques.
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Imagine a world where everyone has enough food, clean water, access to education, and decent work. A world where nature is protected and the future of our planet is cared for. These are the Sustainable Development Goals—to achieve a sustainable future for all! To this end, in 2015, the United Nations (UN) defined 17 Sustainable Development Goals (SDGs). The SDGs are a global plan that helps countries and people work together towards a better future. All 193 UN member states have joined the plan.
Researchers from the Faculty of Artificial Intelligence at RUDN University conducted a large-scale study that revealed systemic errors in large language models (LLMs) when diagnosing depression based on text. This work, carried out in collaboration with colleagues from AIRI, Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Ivannikov Institute for System Programming of the Russian Academy of Sciences, Moscow Institute of Physics and Technology, and MBZUAI, not only identifies the problem but also lays the foundation for the creation of more reliable and secure tools for detecting depression and anxiety.
Alexandra Sentyabreva, a junior researcher at the Laboratory of Cell Technologies and Tissue Engineering at RUDN Research Institute of Molecular and Cellular Medicine at the Russian University of People's Friendship, won the competition for young scientists at the All-Russian Scientific Conference “Topical Issues of Morphogenesis in Norm and Pathology.” She was awarded the Academician A.P. Avtsyn Prize.