KF (Kalman Filter) tracking method based on fading memory exponential weighting

A Kalman filtering and exponential weighting technology, which is applied in the direction of navigation, measuring devices, instruments, etc. through velocity/acceleration measurement, can solve the problem of large calculation errors of fading factors, reduce the accuracy of algorithm estimation, and reduce the correction effect of innovation covariance matrix And other issues

Inactive Publication Date: 2019-01-08
GUANGXI UNIVERSITY OF TECHNOLOGY +1
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Problems solved by technology

[0006] Based on this, it is necessary to focus on the traditional fading factor Kalman filter tracking method. The fading factor calculation method reduces the correction effect of the latest observation information on the innovation covariance matrix, reduces the estimation accuracy of the algorithm, and leads to the calculation of the fading factor. For the problem of large errors, a Kalman filter tracking method based on fading memory index weighting is provided

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  • KF (Kalman Filter) tracking method based on fading memory exponential weighting
  • KF (Kalman Filter) tracking method based on fading memory exponential weighting
  • KF (Kalman Filter) tracking method based on fading memory exponential weighting

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Embodiment 1

[0108] Embodiment one validity analysis

[0109] In order to analyze the effectiveness of the method in this paper, the filter estimation is first carried out in the case of matching the statistical characteristics of the noise, and the data in the simulation experiment is set to R k =Q k =diag[0.01 0.01 0.01 0.01], and the setting is also considered to be satisfied during the simulation process, and the filtering tracking results are as follows figure 1 and figure 2 shown. From figure 1 and figure 2 It can be seen from the figure that in the case of noise matching, the three methods can achieve good estimation, and the overall errors of the three methods are all controlled within 0.04, which can meet the high-precision estimation requirements, and verify the correction of the method in this paper. It's effective. Further, from figure 2 It can be seen that the estimation accuracy of the method in this paper and the fading factor KF method is slightly better than that...

Embodiment 2

[0110] Embodiment two superiority analysis

[0111] In order to analyze the superiority of the method estimated in this paper in the case of mismatch, the noise parameters of the actual data are amplified by 10 times and 50 times respectively, but in the actual algorithm parameters, the noise characteristics are still considered to meet the standard of formula (26), and the noise algorithm is the same as the actual The noise statistical characteristics of the environment differ by 10 times, and the filtering and tracking results are as follows: image 3 to map Figure 6 shown.

[0112] From image 3 and Figure 4 It can be seen from the figure that when the state noise mismatch is 10 times, both the fading factor KF method and the method in this paper maintain a high estimation accuracy, and the absolute value of the estimation error is controlled within 0.04m, while the estimation error of KF The absolute value exceeds 0.04m. It shows that the method in this paper and th...

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Abstract

The invention provides a KF (Kalman Filter) tracking method based on fading memory exponential weighting. The method comprises the following steps: a state error covariance matrix P and a systematic process noise matrix are acquired; an estimated predictive state parameter value shown in the description of a moving object at the moment k is calculated, and innovation covariance C0,k at the momentk is calculated; innovation gamma k at the moment k is calculated, an estimated innovation covariance value shown in the description at the moment k is calculated, weighting coefficient beta k at themoment k is calculated, and the fading factor lambda k at the moment k is further calculated; a predictive state error covariance matrix Pk|k-1 and Kalman gain Kk at the moment k are calculated, and an estimated state value shown in the description and a state error covariance matrix Pk are further calculated, wherein a calculation method for the estimated innovation covariance value at the momentk is shown in the description, and the weighting coefficient [beta i] decays following the law of negative exponent. The problem of poorer precision of the traditional windowing average method for calculating innovation residual vector estimation is solved, and innovation residual estimation precision is improved effectively, so that the method has higher precision and robustness.

Description

technical field [0001] The invention relates to the technical field of Kalman filtering, in particular to a tracking method of Kalman filtering based on fading memory index weighting. Background technique [0002] As an optimal recursive estimation method for linear and Gaussian systems, the Kalman Filter (KF) method has the advantages of high precision and low computational complexity. Since it was proposed in 1960, it has been used in state estimation, target tracking, navigation guidance , mechanical control and other fields have been widely and deeply applied and researched. The main idea of ​​KF is to recursively estimate the state at the next moment based on the current estimated value and current observation information. The traditional Kalman filtering method requires the construction of an accurate system model, including the statistical characteristic model of the system's state noise and observation noise. If the statistical characteristics of the noise do not ma...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01C21/16
CPCG01C21/165
Inventor 黄镇谨李瑞娟马立军封旭黄力
Owner GUANGXI UNIVERSITY OF TECHNOLOGY
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