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Fractional Kalman filter method for processing Levy noise

A fractional, noisy technique used in system analysis and processing to solve problems that are difficult to satisfy

Inactive Publication Date: 2016-09-07
NANJING UNIV OF TECH
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AI Technical Summary

Problems solved by technology

The traditional Kalman filtering method requires the system to be of integer order, and both the system noise and the measurement noise are Gaussian white noise. These ideal requirements are difficult to meet in the actual system
In contrast, real systems are often affected by non-Gaussian noise, and not all systems can be modeled with integer order

Method used

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  • Fractional Kalman filter method for processing Levy noise
  • Fractional Kalman filter method for processing Levy noise
  • Fractional Kalman filter method for processing Levy noise

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Embodiment Construction

[0039] Below in conjunction with specific embodiment, further illustrate the present invention, should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various equivalent forms of the present invention All modifications fall within the scope defined by the appended claims of the present application.

[0040] like figure 1 As shown, a fractional-order Kalman filter method for processing Lévy noise includes the following steps:

[0041] (1) Initialization, including: setting the initial value of the state prediction quantity and the initial value of the prediction error covariance.

[0042] (2) Process the system noise and measurement noise at the current moment.

[0043] (3) Using the approximate value of system noise and measurement noise, bring it into the original system to get a new fracti...

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Abstract

The invention discloses a fractional Kalman filter method for processing Levy noise. The method includes: firstly, giving a state prediction initial value and a predicted error covariance initial value; secondly, processing the Levy noise, and calculating a current moment measured noise covariance and a current moment optimal filtering gain; thirdly, calculating a current moment state estimated value through the current moment optimal filtering gain and a state prediction value, calculating a current moment estimated error covariance through the predicted error covariance, and calculating a current moment system noise covariance through the current moment state estimated value and the estimated error covariance; and finally, updating the next moment state prediction value through the current moment state estimated value, and updating the next moment predicted error covariance through the current moment estimated error covariance. The method can solve the problem of state estimation of a fractional linear discrete system in non-gaussian noise, and is easy to combine with existing state estimation software.

Description

technical field [0001] The invention relates to a fractional-order Kalman filtering method for processing Lévy noise, belonging to the technical field of system analysis and processing. Background technique [0002] System analysis and processing aims to study the interaction of various parts (subsystems) in a specific system structure, the external interface and interface of the system, as well as the behavior, functions and limitations of the system as a whole, so as to provide information for the future changes of the system and related decisions. One of the goals is to improve the decision-making process and system performance, so as to achieve the overall optimum of the system. In the field of system analysis and processing, state estimation plays a vital role. According to the relative relationship between the observed data and the estimated state in time, the state estimation can be divided into three situations: smoothing, filtering and prediction. To estimate the ...

Claims

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Application Information

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IPC IPC(8): G06F19/00
CPCG16Z99/00
Inventor 沈谋全周玉庭严沈陈爱华
Owner NANJING UNIV OF TECH
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