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A Robust Estimation Algorithm Based on Quasi-quasi Test

A technology of robust estimation and pollution rate, applied in computing, computer components, design optimization/simulation, etc., can solve problems such as the selection of quasi-observed values ​​that cannot be effectively automated, and the collapse of model parameter estimation

Active Publication Date: 2021-09-10
CHANGAN UNIV
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Problems solved by technology

The key to this method is the selection of quasi-observations. When the proportion of gross errors is too high, the existing technology cannot effectively and automatically select the quasi-observations, which leads to the collapse of the model parameter estimation.

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  • A Robust Estimation Algorithm Based on Quasi-quasi Test
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  • A Robust Estimation Algorithm Based on Quasi-quasi Test

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Embodiment Construction

[0044]The present invention will be further described below in conjunction with the accompanying drawings.

[0045] refer to figure 1 , the specific implementation steps of the present invention are as follows:

[0046] Step 1. Based on all the GNSS horizontal velocity fields in the research area, establish the overall rotation and uniform strain model, and solve the model parameters and the corresponding least squares correction number V according to the least squares principle;

[0047] Wherein, the model of the overall rotation and uniform strain is:

[0048]

[0049] In the above formula, L is the observation vector, including eastward and northward velocities; V e , V n Respectively represent the eastward and northward velocity; R' represents the radius of the earth; Indicates the latitude and longitude position of the station; Indicates the geometric center of the study area; ε ee , ε en , ε nn Represents three principal strain parameters, which are east-wes...

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Abstract

The invention discloses a super-strong collapse pollution rate robustness estimation algorithm based on quasi-quasi-test, which realizes the automatic selection of quasi-quasi-observed values ​​in the case of any gross error ratio by using the K-means clustering algorithm, so as to realize gross error Rough identification, and then iterative calculation is performed with the quasi-true error as the initial value of the equivalent weight function in the robust estimation, so as to realize the fine identification of the gross error and the robust estimation of the super-collapse contamination of the model parameters. Compared with the conventional robust estimation and the robust estimation based on the residual median, this method can more accurately detect gross error data in the regional GNSS velocity field, and achieve a super collapse of the parameters of the regional crustal movement model The anti-error estimation of pollution rate provides more real and valuable basic data for further research on regional crustal deformation characteristics, and provides an effective processing method for gross error detection and model parameter estimation of crustal deformation monitoring data in complex scenes.

Description

technical field [0001] The invention belongs to the field of high-precision crustal deformation monitoring data processing, and relates to a GNSS horizontal velocity field gross error detection and model parameter estimation technology based on quasi-quasi-verification. The algorithm takes high-precision crustal deformation monitoring as the actual application background and can be used The application direction of high-precision monitoring of crustal deformation in scenarios where the observation environment is complex and local crustal activities are active. Background technique [0002] With the rapid development of modern space geodetic technology, especially the modernization of space monitoring technology represented by the Global Navigation Satellite System (GNSS), it can be used to achieve high-precision horizontal movement of the earth's crust with centimeter-level or even millimeter-level precision. monitor. However, due to factors such as monitoring environment i...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F30/20G06K9/62
CPCG06F30/20G06F18/23213
Inventor 瞿伟陈海禄张勤高源梁世川韩亚茜
Owner CHANGAN UNIV