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A Gyroscope Filtering Method Based on Sage-Husa Kalman Filter

A Kalman filter and gyroscope technology, which is applied in low-cost gyroscope output signal processing and inertial navigation, can solve problems such as lack of versatility and cumbersome Kalman filter steps, and achieve stable and reliable algorithms, ensure noise estimation accuracy, Improve the effect of filtering and stability

Active Publication Date: 2021-11-05
BEIHANG UNIV
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Problems solved by technology

[0004] In order to solve the problem that the steps of Kalman filtering using time series analysis and modeling are cumbersome and not universal, the present invention proposes a simple modeling Sage-Husa Kalman filtering algorithm to filter the output data of the gyroscope

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  • A Gyroscope Filtering Method Based on Sage-Husa Kalman Filter

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Embodiment Construction

[0051] The present invention will be further described below in conjunction with the accompanying drawings.

[0052] (1) if figure 1 As shown, for the traditional Kalman filter or the Sage-Husa Kalman filter, the state equation and measurement equation of the system must be established first, and then the initial value of the filter is set to enter the solution iteration cycle.

[0053]For the gyroscope used in drones, because the navigation and control system has high requirements for real-time performance, the sampling frequency of the gyroscope is extremely fast, and the data change rate of the gyroscope between two data samples is 0; the noise value is filtered when filtering As part of the state quantity to improve the filtering effect and stability, the state equation and measurement equation are obtained:

[0054]

[0055]

[0056] One-step state transition matrix obtained from the above formula ε(k-1) is white noise with a mean value of zero; Z(k) is the angul...

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Abstract

The invention provides a gyroscope filtering method based on Sage-Husa Kalman filtering. The gyroscope is an important device for inertial navigation solution in unmanned aerial vehicles, and is also a core component of attitude solution, and attitude solution is the follow-up velocity and The basis for position calculations. The traditional filtering method is Kalman filtering for low-pass filtering and time series modeling, but there are problems: the delay of low-pass filtering is serious; the noise matrix in time series modeling can only represent the characteristics of a period of time under static conditions , and for different devices, it is necessary to re-analyze and model, the procedure is cumbersome and not universal. This method adopts the Sage-Husa adaptive Kalman filter. By analyzing the characteristics of the gyroscope, simple modeling is carried out, which saves the cumbersome and non-universal time series analysis and modeling, and processes the noise as a filter state quantity, which can It can better suppress the random drift of the system.

Description

technical field [0001] The invention mainly relates to the technical field of low-cost gyroscope output signal processing, belongs to the technical fields of inertial navigation, unmanned aerial vehicle navigation, etc., and specifically relates to a gyroscope filtering method based on Sage-Husa Kalman filtering. Background technique [0002] Low-cost gyroscopes are now commonly used in industries such as mobile phones, smart wearables, and drones. It is the core device of inertial navigation attitude calculation, and its random noise has a great impact on navigation calculation. [0003] For gyroscope data processing, the simplest methods are low-pass filtering and sliding window filtering. The advantage is that it is simple to implement and does not require a system model. However, when the effect is strong, the delay is more serious. At present, the more common method is to collect the gyroscope data offline, conduct time series analysis, obtain the state equation, and t...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01C25/00
CPCG01C25/00
Inventor 胡少兴李晓东田博显肖深
Owner BEIHANG UNIV
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