This paper concerns the problem of quantifying the observability of different states in linear (or linearized) time-varying systems with focus on the usability to integrated navigation. The main goal is the derivation of a novel scalar method to measure the degree of observability in order to quantitatively analyze the observability of each state variable of a system. The new proposed observability analysis method is easily employed in practice with less computer memory and clearer physical meanings. The application to integrated navigation systems was illustrated to analyze the observability properties of navigation error-states. According to the simulation results, the degree of observability can help us to quantitatively compare the observability of each state variable and further function as an indicator to analyze the influence of physical model parameters on the observability and estimability of navigation errors.

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The article discusses two approaches to modeling signals and processes: the method of filter construction and the trigonometric method. It is shown that the later approach is more promising, since an increase in the signal/process representation dimension mathematically means adding a term to the basis function formula, which gives access to fast simulation algorithms. Examples of algorithms for multidimensional simulation of random processes using two methods are given and a software system that implements these algorithms is described. The results provided by the software system will allow you to predict characteristics of engineering projects (accuracy and speed of modeling algorithms). Due to the high relevance of and need for fundamental research of methods and algorithms for digital transformation of the component base, the digitalization of all aspects of activity, including the synthesis of new materials, the development of new methods for designing micro- and nano-systems, the article aims to expand the scope of the spectral method of simulating multidimensional processes using original algorithmic complexes.

The paper presents a method of developing a variable structure measurement system with intelligent components for flight vehicles. In order to find a distinguishing feature of a variable structure, a numerical criterion for selecting measuring sensors is proposed by quantifying the observability of different states of the system. Based on the Peter K. Anokhin’s theory of functional systems, a mechanism of “action acceptor” is built with intelligent components, e.g. self-organization algorithms. In this mechanism, firstly, prediction models of system states are constructed using self-organization algorithms; secondly, the predicted and measured values are compared; thirdly, an optimal structure of the measurement system is finally determined based on the results of comparison. According to the results of simulation with practical data and experiments obtained during field tests, the novel developed measurement system has the properties of high-accuracy, reliable operation and fault tolerance.

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