The paper considers a method for implementing an adaptive extended Kalman filter (EKF) for BINS on SBC/MCU class onboard platforms under constraints on computing resources and power consumption. Simulation modeling is performed in MATLAB/Simulink with variations in filtration frequency, adaptation window, and matrix operation structure. Configurations are compared in terms of navigation accuracy, adaptation stability, and computational cost in a series of Monte Carlo experiments. Additionally, the results are interpreted in terms of operational efficiency: an increase in computational load and energy consumption is considered as a factor increasing the requirements for onboard power and the cost of the hardware platform, and a deterioration in adaptation stability is considered as a source of potential operational losses due to degradation of navigation quality. The results are presented in graphs, tables, and Pareto analysis; based on them, practical recommendations are formed for 50, 100, and 200 Hz modes, taking into account the target platform class.
Platform-free inertial navigation system, BINS, adaptive extended Kalman filter, EKF, MEMS sensors, MATLAB/Simulink, simulation modeling, Monte Carlo, computational cost, energy consumption, operational losses, economic efficiency, Pareto analysis, onboard computers, SBC, MCU
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