Kalman filtering based quadrotor unmanned aerial vehicle attitude data fusion method

A Kalman filtering and data fusion technology, applied in navigation computing tools and other directions, can solve problems such as inability to obtain attitude data in real time and large amount of computation

Inactive Publication Date: 2015-12-09
HARBIN INST OF TECH
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Problems solved by technology

[0004] In order to solve the problem that the existing Kalman filter is too large to obtain the attitude data in real time during the attitude calculat

Method used

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  • Kalman filtering based quadrotor unmanned aerial vehicle attitude data fusion method
  • Kalman filtering based quadrotor unmanned aerial vehicle attitude data fusion method
  • Kalman filtering based quadrotor unmanned aerial vehicle attitude data fusion method

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specific Embodiment approach 1

[0049] Specific embodiments one, a kind of method for the fusion of attitude data of quadrotor UAV based on Kalman filter described in this embodiment, carry out according to the following steps:

[0050] 1. According to the characteristics of the attitude measurement sensor of the quadrotor drone, select the actual attitude angle θ of the quadrotor drone and the measurement error σ of the gyroscope as the state vector, and the attitude angle θ measured by the acceleration sensor and the magnetic sensor 测 is the observed value, and the corresponding state equation and observation equation of the quadrotor UAV attitude calculation system are obtained, as shown in formula (1),

[0051]

[0052] in is the differential of the actual attitude angle θ of the quadrotor UAV, is the differential of the measurement error σ of the gyroscope, ω is the angular velocity output by the gyroscope, ω 白 is the measurement white noise of the gyroscope, v 白 is the measurement white noise o...

specific Embodiment approach 2

[0090] Specific embodiment 2. This embodiment is a further description of a method for the fusion of attitude data of a quadrotor UAV based on Kalman filtering described in the specific embodiment 1. The actual attitude angle θ described in step 1 is quadrotor The vector angle formed by the drone's actual roll angle, pitch angle, and yaw angle.

[0091] The actual attitude angle θ cannot be directly measured by measuring instruments, only by actually measuring the roll angle, pitch angle and yaw angle of the UAV, and expressing the actual attitude angle θ in the form of a vector.

specific Embodiment approach 3

[0092] Specific embodiment three. This embodiment is a further description of the method for the fusion of attitude data of a quadrotor UAV based on Kalman filtering described in the specific embodiment one. E described in step thirteen A (k), E B (k) and K g (k) is evaluated offline, and the evaluation result is stored in the processor chip of the flight control board.

[0093] E. A (k), E B (k) and K g The evaluation of (k) adopts an off-line method, which greatly reduces the calculation amount of the flight control board processor.

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Abstract

The invention relates to the field of multi-sensor data fusion in integrated navigation, in particular to a Kalman filtering based quadrotor unmanned aerial vehicle attitude data fusion improved method, and aims to solve the existing problem that due to heavy computation of Kalman filtering in a quadrotor unmanned aerial vehicle attitude computation process, attitude data cannot be acquired in real time. The method provided by the invention improves the Kalman filtering equation set, i.e. improves the current state prediction equation, the current state error covariance prediction equation, the current optimal attitude angle equation, the Kalman gain Kg(k) equation and the covariance equation of the current optimal attitude angle equation, realizes off-line computation of partial data, and greatly reduces the computation amount of a flight control board processor, thereby meeting the real-time requirement and data accuracy requirement of quadrotor unmanned aerial vehicle attitude data. The method provided by the invention can be applied to the technical field of multi-sensor data fusion in integrated navigation.

Description

technical field [0001] The invention relates to the field of multi-sensor data fusion in integrated navigation, in particular to a method for fusion of attitude data of a quadrotor UAV based on Kalman filtering. Background technique [0002] Kalman filtering is an optimal autoregressive data processing algorithm. In recent years, it has been widely used in computer image processing. In the quadrotor UAV flight system, it is necessary to integrate and correct the data measured by the gyroscope, acceleration sensor and magnetometer sensor. If a simple mean filter is used, it is difficult to meet the requirements of accuracy and real-time performance. Therefore, the data of the acceleration sensor, the magnetic sensor and the gyroscope are fused through the Kalman filter, and the interference of the noise is well suppressed when calculating the real-time attitude of the quadrotor UAV aircraft, so as to improve the measurement accuracy and provide the basis for autonomous flig...

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Application Information

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IPC IPC(8): G01C21/20
CPCG01C21/20
Inventor 陈兴林罗文嘉李松高怡然徐川川于志亮崔宁杨昱昊
Owner HARBIN INST OF TECH
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