Unmanned aerial vehicle anti-interference passive positioning method and system based on GRU and particle filter fusion

By using a fusion method of GRU and particle filtering, a state proposal distribution for the UAV is generated, which solves the problem of unstable UAV positioning in complex environments and achieves high-precision and continuous positioning results.

CN121741633APending Publication Date: 2026-03-27ZHUHAI LI CHUANG KE XIN INVESTMENT PARTNERSHIP (LLP) +1
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Patent Information

Application Number
CN202511570885.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-10-30
Publication Date
2026-03-27

AI Technical Summary

Technical Problem

Existing UAV positioning methods are unstable in complex environments and prone to disconnection. Traditional filtering methods are difficult to accurately describe the complex motion patterns of UAVs, particle filtering algorithms have low computational efficiency, and TDOA signals are susceptible to interference, leading to increased positioning errors and reduced real-time performance.

Method used

The proposed distribution is generated by combining GRU neural network with particle filtering. Multiple pre-trained GRU networks are used to generate the state proposal distribution of the UAV in the next time step. The state prediction is performed by combining the particle filtering algorithm. The sequence learning capability of GRU and the framework of particle filtering are used for efficient fusion.

Benefits of technology

It achieves continuous, smooth, and high-precision positioning of UAVs in complex environments, reduces computational load, improves positioning accuracy and trajectory smoothness, has strong anti-interference capabilities, and ensures the continuity of positioning.

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Abstract

The invention relates to an unmanned aerial vehicle anti-interference passive positioning method and system based on GRU and particle filtering fusion, belongs to the field of navigation and positioning, and solves the problems that an unmanned aerial vehicle positioning signal is unstable and is easy to drop in a complex environment in the existing method. Comprising the following steps: acquiring the time difference of arrival of radio communication signals of at least one unmanned aerial vehicle by using a plurality of TDOA passive positioning nodes, and generating a historical state sequence of each unmanned aerial vehicle based on the time difference of arrival; wherein the state value in each historical state sequence is a TDOA observation value and comprises three-dimensional coordinate data and three-dimensional speed data; inputting the historical state sequence of each unmanned aerial vehicle into a plurality of pre-trained GRU neural networks, and generating proposed distribution of the state of each unmanned aerial vehicle at the next moment based on the GRU state sequences output by the plurality of networks; and obtaining a state prediction result of each unmanned aerial vehicle based on a proposal distribution fusion particle filtering algorithm of the state of each unmanned aerial vehicle at the next moment. And continuous, smooth and high-precision positioning of the unmanned aerial vehicle under the condition of discontinuous signals is realized.
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