A weighted probability likelihood GNSS shadow matching positioning method and system based on a spiral search algorithm

By optimizing GNSS positioning using a spiral search algorithm and a weighted probability likelihood scoring mechanism, the problem of high computational cost and low efficiency of traditional shadow matching in complex environments is solved, achieving high-precision and high-efficiency positioning optimization.

CN122110171APending Publication Date: 2026-05-29HARBIN INST OF TECH AT WEIHAI
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
HARBIN INST OF TECH AT WEIHAI
Filing Date
2026-02-11
Publication Date
2026-05-29

AI Technical Summary

Technical Problem

Traditional shadow matching suffers from high computational cost, low computational efficiency, and low positioning accuracy in complex environments.

Method used

A weighted probability likelihood GNSS shadow matching method based on a spiral search algorithm is adopted. By starting from the initial position and performing a spiral search, combined with satellite visibility prediction and a weighted probability likelihood scoring mechanism, the candidate point scores are optimized, the impact of noise is reduced, and the weights are dynamically adjusted.

Benefits of technology

It significantly improved the positioning accuracy and computational efficiency in urban canyon environments, reduced the number of candidate points, lowered computational complexity, and reduced the positioning error from 36.1 meters to 6.2 meters, achieving efficient positioning optimization.

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Abstract

The application provides a weighted probability likelihood GNSS shadow matching positioning method and system based on a spiral search algorithm, and belongs to the technical field of GNSS positioning. In order to solve the problems of high calculation cost, low calculation efficiency and low positioning accuracy of traditional shadow matching in a large positioning uncertainty area, the application performs spiral search with an initial position as a starting search point, reduces the calculation amount compared with a traditional method, and greatly reduces the number of candidate points compared with an exhaustive grid search. The application can converge faster and find a high-quality positioning position solution faster. In addition, the application introduces a weighted probability likelihood method to improve the shadow sensitivity by setting a larger weight for a low-elevation satellite in practical applications such as urban environments, to realize dynamic adjustment by setting a medium weight distribution in suburban environments, and to realize real-time adjustment according to satellite geometry, so that the flexibility is better, and the positioning in a complex environment can be better realized.
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