Filtering method for IMU multi sensor data fusion

Abstract

The present invention provides a filtering method for IMU multi sensor data fusion, firstly data is collected, aircraft attitude data can be acquired by an IMU inertial measurement unit comprising a gyroscope, an accelerometer and other multiple sensors, and a sensor output information mathematical model expression formula can be established; output data processing is performed, and the output data processing mainly includes removal of interference information and noises by use of slide average filter and resolution of the rotation angle of each sensor based on a four element method; a high-pass filter and a low-pass filter are combined, PI control parameters are added into a low-pass filtering part, proportionality coefficient variable Kp and integral coefficient variable KI are introduced for design of an error correction negative feedback of a second order complementary filter so as to realize the data fusion of each sensor, and finally attitude angles are outputted. By introduction and setting of the PI control parameters, a negative-feedback-containing second order complementary filter algorithm is designed to fuse the information of each sensor to improve the measurement accuracy and dynamic performance of the system.

Description

technical field [0001] The invention belongs to the field of digital image processing and relates to a filtering method, in particular to a filtering method for IMU multi-sensor data fusion. Background technique [0002] With the development of micro-electro-mechanical system technology, in order to meet the needs of a wide range of applications, micro-machined gyroscopes, accelerometers, magnetometers, etc. are used to construct a small and low-cost attitude reference system. At the same time, micro UAVs have the characteristics of low cost, good concealment, and strong survivability. In recent years, they have been more and more used in military, civilian and other related fields. Attitude measurement is the prerequisite for UAV to achieve attitude control, and it is also an inseparable and important part of the navigation system. The attitude of UAV aircraft mainly includes two parts: attitude calculation and flight control. Among them, attitude calculation is the core a...

AI Technical Summary

Problems solved by technology

The particle filter attitude estimation method improves the filtering accuracy and solves the influence of system nonlinearity and non-white noise on the attitude calculation, but the calculation amount is large, and it is not suitable for low-cost attitude system

The performance of the gradient descent method is better than that of the Kalman filter in attitude calculation, with less calculation and low requirements for the initial point, but the convergence speed is slower

The principle of the complementary filtering method is simple, the amount of calculation is small, and the noise is eliminated through the flat rate resolution, which can better combine the static accuracy of the accelerometer and the high dynamic attitude data of the gyroscope, and use the complementary characteristics of the two angles in the frequency domain to improve the attitude measurement. Accuracy and dynamic response to reduce measurement and estimation errors, but the transfer frequency of its high-pass filter and low-pass filter is difficult to determine

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