Battlefield target tracking method and device based on hybrid multi-source information fusion

CN121030639BActive Publication Date: 2026-06-30UNIV OF SCI & TECH BEIJING
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Patent Information

Application Number
CN202511098438.2
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-08-06
Publication Date
2026-06-30
Estimated Expiration
2045-08-06

AI Technical Summary

Technical Problem

In modern battlefield environments, the heterogeneity of multi-source sensors, uneven sensor distribution, unstable communication links, and easy failure of nodes lead to target loss, false alarms, and delayed responses, making it difficult to meet the requirements of high real-time performance, high fault tolerance, and local autonomous response.

Method used

A hybrid multi-source information fusion method is adopted, which constructs a two-layer fusion architecture that combines distributed autonomy and centralized optimization through time registration, spatial registration, distributed information fusion and global centralized information fusion, so as to achieve high-precision tracking of battlefield targets and tactical decision-making.

Benefits of technology

It significantly enhances the system's resilience and responsiveness, enabling it to continuously track targets even when some nodes are out of contact or under attack, thus meeting the needs for continuous perception and decision support in the fast-paced and high-pressure battlefield environment.

✦ Generated by Eureka AI based on patent content.

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Abstract

This invention relates to the field of battlefield information fusion technology, and particularly to a battlefield target tracking method and apparatus based on hybrid multi-source information fusion. The method includes: a distributed fusion stage employing a non-repetitive diffusion mechanism, which effectively avoids path loops and data redundancy by restricting unidirectional information propagation within the neighborhood, significantly reducing communication load and preventing artificially high confidence levels, thus improving the system's fusion stability and local state estimation accuracy under resource-constrained conditions. Each node uploads its local fusion results to the command center, where centralized fusion further optimizes the global target state estimation, enhancing fusion accuracy and consistency. Based on this, combined with a multi-index task decision model, the command center can dynamically schedule attack, tracking, and jamming mission strategies according to the target state. This method has advantages such as low communication load, low central dependency, and strong system robustness, enabling continuous and stable target tracking and intelligent tactical response in complex battlefield environments.
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