To reduce the influence of MEMS gyroscope random errors on navigation systems, the improved variational mode decomposition-wavelet threshold de-noising (WTD) method is proposed in this paper. First, to suppress the endpoint effect caused by the signal truncation and Hilbert transform during decomposition, the triangular waveform matching method is used to search for the waveform which most matches the endpoints in the whole signal. Secondly, the grid search algorithm is used to select the optimal parameters for the extended signal to realize the optimal decomposition. Finally, all the components are analyzed to determine autocorrelation characteristics. According to the variance of the autocorrelation function, all the components can be divided into noise components, mixed components and signal components. The noise components are directly removed, and the mixed components are de-noised using the WTD method. Then, the signal components and the mixed components are reconstructed after de-noising. Analysis of simulation and measured data de-noising experiments proves the effectiveness of the proposed method. For the static signal, the mean square error (MSE) of the proposed method is reduced by 10.1% and the signal to noise ratio (SNR) is increased by 14.2%. For the dynamic signal, the MSE of the proposed method decreases by 16.9% and the SNR increases by 18.8%.

Compared with the non-redundant inertial navigation system (INS), the redundant INS (RINS) has more error parameters and the system is more complicated. The calibration methods for non-redundant INS are unable to be adopted by RINS directly. Meanwhile, the inner lever arm effect of accelerometers is more severe in RINS, which is not supposed to be ignored in the error compensation. To solve the problems above, this paper proposes a novel calibration method for accelerometers in RINS. First, the paper analyses and models the bias, scale factor error, installation angle error and lever arm error. Based on the error model, two Kalman filters are designed to estimate the error parameters. The calibration is divided into two steps: the bias, scale factor error and installation angle error are calibrated by the static multi-position experiment first, and then the lever arm error is calibrated by the rotation experiment. Experiments prove that the proposed method can effectively calibrate the deterministic error of the accelerometers, that the estimation errors are controlled at 1 × 10 −4 level. Further, the paper studies and optimizes the turntable rotation scheme in the lever arm error calibration experiment, and proposes the design principles for the rotation scheme. Comparing with the casually designed scheme, the optimized procedure can improve the accuracy by almost an order of magnitude as well as the time-consumption being shortened by 40%. The design principles are applicable to the other inertial navigation systems to improve the calibration accuracy and reduces the time cost.

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