The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
May. 16, 2023

Filed:

Dec. 02, 2020
Applicant:

Southeast University, Nanjing, CN;

Inventors:

Xiyuan Chen, Nanjing, CN;

Xin Shao, Nanjing, CN;

Xinhua Tang, Nanjing, CN;

Xuefen Zhu, Nanjing, CN;

Xiaosu Xu, Nanjing, CN;

Assignee:

SOUTHEAST UNIVERSITY, Nanjing, CN;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G01C 25/00 (2006.01); B60W 30/18 (2012.01); G06F 17/16 (2006.01); G06F 17/17 (2006.01); G01C 21/16 (2006.01);
U.S. Cl.
CPC ...
G01C 25/005 (2013.01); B60W 30/18018 (2013.01); G01C 21/188 (2020.08); G06F 17/16 (2013.01); G06F 17/175 (2013.01);
Abstract

A strict reverse navigation method for optimal estimation of fine alignment is provided. The strict reverse navigation method including: establishing an adaptive control function; performing a forward navigation calculation process; performing a reverse navigation calculation process; and performing the adaptive control for a number of forward and reverse calculations. The strict reverse navigation method shortens an alignment time for the optimal estimation of fine alignment while ensuring an alignment accuracy. The strict reverse navigation method provided effectively solves a problem that an error of an initial value of filtering in an initial stage of the optimal estimation of fine alignment affects convergence speeds of subsequent stages. In the initial stage, a larger number of the forward and reverse navigation calculations are adopted to reduce an error of the initial value as much as possible and increase a convergence speed of the filtering.


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