PARAMETRIC SYNTHESIS OF FILTERING ALGORITHMS FOR INERTIAL NAVIGATION SYSTEMS OF UNMANNED AIRCRAFT

In inertial navigation systems of unmanned aerial vehicles, filtering algorithms, in particular the Kalman filter and its various modifications, are used to increase the accuracy of navigation measurements. The practical implementation of such filtering algorithms is complicated by their computational complexity and abstract form of representation, which does not reflect all the features of the implementation. That is why the task of synthesizing filtering algorithms that will meet the requirements of guaranteed convergence of the filtering process and minimal computational complexity of their implementation is relevant. The latter requirement is extremely important for navigation systems of small unmanned aerial vehicles, since their on-board equipment must be cheap and low-energy. In this regard, the article presents a parametric synthesis of an optimal algorithm for polynomial filtering of the measurement results of accelerometric orientation sensors in inertial navigation systems of unmanned aerial vehicles. Parametric synthesis refers to the determination of optimal internal parameters, which are the smoothing coefficients of the filter. The synthesis of parameters is performed under the condition of a given filter structure. The filter was synthesized using a method based on the invariance theory and described by the authors in previous works. The optimal smoothing coefficients were determined by conditional optimization of the objective function, which was chosen as the minimum of the mean square error of the estimation. The filter stability conditions, which are determined by an algebraic criterion, are chosen as constraints. Due to the scalar form of implementation, the synthesized optimal filtering algorithm has a low computational complexity. The effectiveness of the algorithm was confirmed by computer simulation based on the results of real measurements of the ADXL345 accelerometer, which is part of the Arduino UNO R3.