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Abstract <p>Jamming and spoofing of signals from global navigation satellite systems (GNSS) currently the most effective in accurately positioning moving objects necessitates the search for alternative navigation methods that ensure both accuracy and autonomy. Such approaches include various methods of integrating inertial navigation systems (INSs) with optical navigation systems (ONSs), as they offer the highest accuracy over long distances and are the least expensive. For objects moving along arbitrary trajectories (when terrain maps and reference points are unavailable), existing methods for processing information obtained from ONSs in the form of an optical flow velocity field enable the estimation of projections of the object’s current velocity (both linear velocity and angular velocity). However, the use of traditional methods, firstly, does not solve the navigation problem as a whole (determining the object’s coordinates and its spatial orientation angles), and secondly, leads to computational costs that are often critical for onboard computers. In this regard, we examined an approach combining the capabilities of an INS, which provides a solution to the problem of autonomous navigation as a whole, and a developed optical observer of object motion parameters, enabling autonomous estimation of navigation parameters without pre-calculating the velocity field, i.e., with minimal computational effort. Cases of a rigidly mounted video camera on the object and its biaxial and triaxial stabilization were considered to more fully highlight the problem. The final synthesis of algorithms for stochastic estimation of object motion parameters, which considers the uncertainty of the probabilistic characteristics of noise in a real inertial-and-optical navigation system, is carried out with consideration for the subsequent application of methods from modern nonlinear filtering theory. Due to the specific features during the development of the inertial-and-optical navigation system for noise correlation between the object and the observer, the vector of navigation parameters is stochastically estimated using a variant of the extended Kalman filter for correlated noise. The results of numerical simulation of the navigation algorithm demonstrate the feasibility of its effective practical application.</p>

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Integration of Inertial and Optical Navigation Systems with Spatial Stabilization of a Video Camera

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • S. V. Sokolov,
  • V. A. Pogorelov,
  • S. A. Shvidchenko

摘要

Abstract

Jamming and spoofing of signals from global navigation satellite systems (GNSS) currently the most effective in accurately positioning moving objects necessitates the search for alternative navigation methods that ensure both accuracy and autonomy. Such approaches include various methods of integrating inertial navigation systems (INSs) with optical navigation systems (ONSs), as they offer the highest accuracy over long distances and are the least expensive. For objects moving along arbitrary trajectories (when terrain maps and reference points are unavailable), existing methods for processing information obtained from ONSs in the form of an optical flow velocity field enable the estimation of projections of the object’s current velocity (both linear velocity and angular velocity). However, the use of traditional methods, firstly, does not solve the navigation problem as a whole (determining the object’s coordinates and its spatial orientation angles), and secondly, leads to computational costs that are often critical for onboard computers. In this regard, we examined an approach combining the capabilities of an INS, which provides a solution to the problem of autonomous navigation as a whole, and a developed optical observer of object motion parameters, enabling autonomous estimation of navigation parameters without pre-calculating the velocity field, i.e., with minimal computational effort. Cases of a rigidly mounted video camera on the object and its biaxial and triaxial stabilization were considered to more fully highlight the problem. The final synthesis of algorithms for stochastic estimation of object motion parameters, which considers the uncertainty of the probabilistic characteristics of noise in a real inertial-and-optical navigation system, is carried out with consideration for the subsequent application of methods from modern nonlinear filtering theory. Due to the specific features during the development of the inertial-and-optical navigation system for noise correlation between the object and the observer, the vector of navigation parameters is stochastically estimated using a variant of the extended Kalman filter for correlated noise. The results of numerical simulation of the navigation algorithm demonstrate the feasibility of its effective practical application.