Factor graph co-localization algorithm based on maximum correlation entropy

A maximum correlation entropy, co-location technology, applied in complex mathematical operations, navigation calculation tools, design optimization/simulation, etc., can solve problems such as filter instability and estimation performance degradation

Inactive Publication Date: 2021-05-04
HARBIN INST OF TECH
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Problems solved by technology

In the research of traditional nonlinear co-location algorithms, the EKF algorithm and UKF algorithm are usually used to achieve co-location data fusion, but these data fu...

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  • Factor graph co-localization algorithm based on maximum correlation entropy
  • Factor graph co-localization algorithm based on maximum correlation entropy
  • Factor graph co-localization algorithm based on maximum correlation entropy

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Embodiment Construction

[0073] The present invention will be described in detail below in conjunction with specific implementation examples.

[0074] The present invention provides a factor graph collaborative positioning algorithm based on the maximum correlation entropy criterion, which uses the error formula of correlation entropy as a cost function to process noise with abnormal values, which can better deal with heavy-tailed non-Gaussian observation noise, and is robust Higher reliability, can greatly reduce the influence of heavy tail noise on the positioning accuracy of the system, and improve the performance and stability of the collaborative positioning system. The object of the present invention is achieved through the following steps:

[0075] 1. Establish a co-location algorithm factor graph model based on maximum correlation entropy;

[0076] 2. Perform filter fusion estimation on the position and state information of the system through the maximum correlation entropy criterion.

[007...

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Abstract

The invention designs a factor graph co-localization algorithm based on maximum correlation entropy. The method comprises the following steps: firstly, establishing a co-localization algorithm factor graph model based on the maximum correlation entropy, then utilizing the correlation entropy as reliability information, calculating the correlation entropy of variable nodes in the factor graph model, and calculating position state information when the correlation entropy is maximum through differential operation according to a maximum correlation entropy criterion, and realizing filtering fusion estimation of the position state information of the slave boat. According to the method, under the condition of not changing the measurement precision of the inertial device in the system, the error formula of the correlation entropy is used as the cost function of the system, so that the influence of an abnormal value on the system is effectively inhibited, and the robustness of the system is enhanced.

Description

technical field [0001] The present invention relates to autonomous underwater vehicle (Autonomous Underwater Vehicle, AUV) co-location technology, more precisely a co-location algorithm based on maximum correlation entropy criterion and factor graph theory. When using this method for co-localization of AUVs, since the correlation entropy is a local similarity measure and is not sensitive to outliers, it is suitable as a cost function to deal with noise with outliers, so when dealing with heavy-tailed non-Gaussian observation noise, it is relatively Compared with the traditional nonlinear co-location algorithm, it has better estimation performance and higher robustness. It can greatly reduce the impact of heavy tail noise on the system positioning accuracy, and improve the performance and stability of the co-location system. Background technique [0002] In the AUV co-location system, since ranging and communication are realized by using underwater acoustic equipment, it will...

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Application Information

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IPC IPC(8): G06F30/20G06F17/11G01C21/20
CPCG06F30/20G06F17/11G01C21/20
Inventor 张亚夏秀玮高伟王庆鑫魏健雄
Owner HARBIN INST OF TECH
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