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Quaternion-based attitude resolving method for extended Kalman filter algorithm

A technology of extended Kalman and filtering algorithm, applied in the field of attitude calculation of extended Kalman filtering algorithm, can solve the problem of unknown parameter calculation and so on

Active Publication Date: 2018-06-29
SOUTHEAST UNIV
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Problems solved by technology

[0004] The technical problem to be solved by the present invention is to provide an attitude calculation method based on the extended Kalman filter algorithm based on quaternions, which can greatly simplify the calculation amount and solve the problem of unknown calculation of existing parameters

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  • Quaternion-based attitude resolving method for extended Kalman filter algorithm
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  • Quaternion-based attitude resolving method for extended Kalman filter algorithm

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Embodiment Construction

[0076] Such as figure 1 As shown, a kind of posture solution method of the extended Kalman filter algorithm based on quaternion of the present invention comprises the following steps:

[0077] Step 1: Build an attitude estimation system to obtain multi-axis sensor data in the carrier fixed reference system coordinate system;

[0078] Step 2: filter the collected acceleration data and magnetic induction data, and normalize the data collected by these two sensors;

[0079] The data collected by the three-axis gyroscope is w=[w x w y w z ] T , the data collected by the three-axis accelerometer after normalization is a=[a x a y a z ] T , the data collected by the normalized three-axis magnetometer is m=[m x m y m z ] T ;

[0080] Step 3: Construct the state equation of the carrier system according to the quaternion differential equation and the attitude matrix, and obtain the process noise variance matrix of the system;

[0081] Step 4: Use the fast Gauss-Newto...

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Abstract

The invention discloses a quaternion-based attitude resolving method for an extended Kalman filter algorithm. The method comprises the following steps: acquiring multisensor data of a vector in a fixed coordinate system; filtering data acquired by an accelerometer and a magnetometer, and subjecting the data acquired by the two sensors to normalization processing; constructing a state equation of avector system according to a quaternion differential equation and an attitude matrix, and determining a process noise variance matrix of the system; constructing a systematic observation model by using a rapid Gauss-Newton method, and determining a system measured noise variance matrix; constructing a Kalman filtering recurrence equation according to the constructed system state equation and theobservation model; resolving three attitude angles of the vector by using an optimal quaternion obtained through recurrence. According to the method, the computing capacity can be greatly simplified,and the existing problem that parameters are unclear in computing is solved.

Description

technical field [0001] The invention relates to the technical field of multi-sensor data fusion for moving carrier navigation, in particular to an attitude calculation method based on a quaternion-based extended Kalman filter algorithm. Background technique [0002] In the field of multi-sensor data fusion of moving carrier navigation such as human body motion tracking and aircraft navigation, accurate real-time estimation of carrier attitude has a wide range of applications. The rapid development of low-cost micro-electro-mechanical systems has led to the widespread use of smaller and cheaper inertial sensors. However, the data measured by low-cost inertial measurement units are easily affected by high-frequency noise and time-varying bias, so smoothing and unbiased estimation are required in the sensor data fusion algorithm. In order to obtain a more accurate attitude, the data of the accelerometer and magnetometer are often fused with the angular velocity data output by ...

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Application Information

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IPC IPC(8): G01C21/16G01C21/20
CPCG01C21/165G01C21/20
Inventor 徐晓苏喻增威赵北辰孙晓俊
Owner SOUTHEAST UNIV
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