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Spatial registration method for multi-source ranging sensors based on expectation maximization

A technology of maximizing expectation and ranging sensor, applied in the field of navigation and positioning, it can solve the problems of inability to perform registration and registration, unsatisfactory and other problems, reduce storage space and time cost, improve real-time performance, and improve estimation accuracy. Effect

Inactive Publication Date: 2017-10-20
XI AN JIAOTONG UNIV
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Problems solved by technology

Under the condition that there is only a certain measurement information in multiple sensors, such as the measurement information only has distance measurement information, the above methods often cannot perform registration or the registration effect is not ideal

Method used

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  • Spatial registration method for multi-source ranging sensors based on expectation maximization
  • Spatial registration method for multi-source ranging sensors based on expectation maximization
  • Spatial registration method for multi-source ranging sensors based on expectation maximization

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Embodiment Construction

[0035] The present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.

[0036] The spatial registration algorithm of multi-source ranging sensors based on expectation maximization includes the following steps:

[0037] In the first step, the smoothing value for the first round is initialized. This spatial registration algorithm is aimed at sensor registration and target positioning in a multi-source ranging and positioning environment, such as figure 1 As shown, the algorithm embeds an iterative Kalman filter smoother (IEKS) into the EM algorithm, and the IEKS algorithm requires an initialized state value and covariance. In the entire spatial registration algorithm process, filtering, smoothing, and EM solution are performed in sequence, and the smoothing value of the previous round is needed in the filtering process. In actual situations, the state of the target cannot be obtained directly through measurement, because ...

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Abstract

The present invention provides a multi-source ranging sensor spatial registration method based on expectation maximization. Aiming at the problem of using multi-source ranging sensors for target positioning, the likelihood function is constructed through the state equation and measurement equation of the target, and the likelihood function is However, the expected function is calculated and maximized to obtain the estimated value of the implicit system deviation parameter. In the case of pursuing real-time performance, smaller data storage space and time-consuming calculation, the sliding window expectation maximization algorithm can be used. Simulation results show that the method proposed by the invention has the advantages of stronger anti-noise, less required data, and higher stability and precision.

Description

technical field [0001] The invention belongs to the technical field of navigation and positioning, and relates to a positioning method under the condition that multiple sensors only have ranging information, in particular to a space registration method for multi-source ranging sensors based on an Expectation Maximization (EM) algorithm. Background technique [0002] Multi-sensor spatial registration is mainly to eliminate the systematic bias of the sensors. At present, the methods for spatial registration mainly include sequential processing methods and batch processing methods. Sequential processing methods are mainly real-time estimation methods based on extended Kalman filtering and tasteless filtering, and the calculation amount is relatively small. Batch registration methods mainly include least squares method, generalized least squares method, maximum likelihood method and exact maximum likelihood method. This type of algorithm requires centralized processing of data ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01C21/20
CPCG01C21/20G01C25/00
Inventor 元向辉周学平
Owner XI AN JIAOTONG UNIV
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