Abstract
The case of large azimuth misalignment angles in a strapdown inertial navigation system (SINS) is analyzed, and a method of using the adaptive UPF for the initial alignment is proposed. The filter is based on the idea of a strong tracking filter; through the introduction of the attenuation memory factor to effectively enhance the corrections of the current information residual error on the system, it reduces the influence on the system due to the system simplification, and the uncertainty of noise statistical properties to a certain extent; meanwhile, the UPF particle degradation phenomenon is better overcome. Finally, two kinds of non-linear filters, UPF and adaptive UPF, are adopted in the initial alignment of large azimuth misalignment angles in SINS, and the filtering effects of the two kinds of nonlinear filter on the initial alignment were compared by simulation and turntable experiments. The simulation and turntable experiment results show that the speed and precision of the initial alignment using adaptive UPF for a large azimuth misalignment angle in SINS under the circumstance that the statistical properties of the system noise are certain or not have been improved to some extent.
Highlights
The initial alignment is a key technology in strapdown inertial navigation system (SINS), and the alignment precision and the alignment time are two important indexes which affect the overall system performance
For the case of SINS with a large misalignment angle, the error caused by the rotation order cannot be ignored and the error model of SINS must be re-established according to the large misalignment situation
We select the initial misalignment angle as φ (0) = [1° 1° 10°]T, the feedback correction is not performed during the simulation process in both cases; the simulation results of the alignment error are shown in Figures 1 and 2
Summary
The initial alignment is a key technology in SINS, and the alignment precision and the alignment time are two important indexes which affect the overall system performance. With the application field of navigation systems continuing to expand, most application environments cannot meet the condition that the initial misalignment angle is a large angle and the noise is a Gaussian white noise, so continuing to use the traditional linear navigation system model and Kalman Filter (KF) will produce a greater model error and estimation error, which make the navigation parameters unbelievable [1]. An approach based on a covariance matching criterion is adopted to judge the convergence and divergence situation of the filter, the covariance of the prediction error is revised and the filter gain is adjusted by an approach of introducing an adaptive attenuation factor, achieving the goal that restrains and eliminates the divergence phenomenon in the filter and further improves the filter capability of fast tracking To some extent, it reduces the influence on the system due to system simplification, the uncertain statistical properties of the noise, better overcome the UPF particle degradation phenomenon.
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