Self-adaptive filter method for strapdown inertial/Doppler combined navigation system

Abstract

The invention discloses a self-adaptive filter method for a strapdown inertial / Doppler combined navigation system, which aims to improve the response speed and filter accuracy of a filter under a dynamic condition and improve the positioning accuracy of the strapdown inertial / Doppler combined navigation system. According to the method, a limited window smoother related to innovation covariance is introduced, a gain matrix in a filter can be directly corrected based on a smooth value of the innovation covariance, and a one-step predicted mean square error can be corrected by introducing a regulatory factor, so that the aims of improving the dynamic response speed and the filter accuracy of the filter can be achieved. The self-adaptive filter method disclosed by the invention can be applied to the strapdown inertial / Doppler combined navigation system, and the navigation positioning accuracy of the combined system under a dynamic condition can be effectively improved.

Description

technical field [0001] The invention belongs to the technical field of integrated navigation systems, and in particular relates to an adaptive filtering method of a strapdown inertial / Doppler integrated navigation system. Background technique [0002] Conventional Kalman filtering can estimate state variables such as position, velocity error and platform misalignment angle of integrated navigation system, so it has been widely used in the field of integrated navigation. However, the conventional Kalman filter needs to meet the requirements of accurate model and accurate known noise statistical characteristics. For the strapdown inertial and Doppler integrated navigation system, the system inevitably contains uncertain noise, such as gyro random At this time, the conventional Kalman filter will lose the optimal estimation characteristics; and when the conventional Kalman filter reaches a steady state under certain conditions, the filter parameters will also converge to the st...

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Problems solved by technology

However, the conventional Kalman filter needs to meet the requirements of accurate model and accurate known noise statistical characteristics. For the strapdown inertial and Doppler integrated navigation system, the system inevitably contains uncertain noise, such as gyro random At this time, the conventional Kalman filter will lose the optimal estimation characteristics; and when the conventional Kalman filter reaches a steady state under certain conditions, the filter parameters will also converge to the steady state value. If so When the external motion of the system changes drastically, the filter parameters cannot be adjusted immediately, which reduces the filter accuracy and even causes filter divergence.

[0003] In order to solve the problem of inaccurate noise statistical information and the decline of filtering accuracy under dynamic conditions in conventional Kalman filtering, scholars have proposed many adaptive filtering algorithms in recent years, mainly focusing on information adaptive filtering and fading memory attenuation adaptive filtering. filtering both

The document "Adaptive Evanescent Kalman Filter Based on Innovation Covariance, Systems Engineering and Electronic Technology, 2012, Volume 13, No. 12" proposed a method to calculate the fading factor by using the innovation covariance. The method uses the covariance of the innovation sequence to adaptively change the fading factor to adjust the weight of the new information, which reduces the influence of the old measurement value on the estimation, and adjusts the error covariance adaptively through the fading factor to compensate for the incomplete information However, this method needs to obtain two adjustment factors at the same time, which not only increases the amount of calculation, but also because the adjustment factor itself will have a certain error, it may cause a decrease in estimation accuracy, and even cause the divergence of the filter

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