Multi-View UAV Tracking with Radar-Vision Fusion in Low Luminance
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Solution Overview
Problem
Conventional UAV target tracking technologies face challenges in low luminance scenarios due to fuzzy visual information and the reliance on single UAV tracking, which leads to target loss in complex environments.
Innovation Solution
Deploy a UAV cluster equipped with multi-source fusion sensors (millimeter wave radar, RGB visual, and infrared visual sensors) and implement multi-view collaborative tracking using a multi-agent reinforcement learning algorithm for target positioning and trajectory management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If visual data is used as the primary data source for target tracking, then the tracking system is simple to implement, but in low luminance scenarios the visual information becomes fuzzy and insufficient, seriously affecting tracking performance
Solution Approach 1:
The patent combines multiple sensing systems (millimeter wave radar, infrared sensors, and visual sensors) into a unified tracking system. The millimeter wave radar provides reliable distance and velocity information in low luminance conditions, while infrared sensors detect thermal radiation from targets, and visual sensors supplement when conditions permit. This fusion of multiple sensing modalities resolves the contradiction by maintaining tracking reliability across varying luminance conditions while accepting increased system complexity.
Solution Approach 2:
The patent employs a composite sensing architecture that integrates different types of sensors with complementary characteristics. The millimeter wave radar component provides all-weather capability, infrared sensors add thermal contrast detection, and visual sensors provide detailed imagery when available. This composite sensing approach ensures reliable target detection and tracking in low luminance scenarios by leveraging the strengths of each sensor type.
2Device complexity
If a single UAV is used for target tracking, then the device complexity is low, but the target is easily lost in complex environments such as underground caverns due to blocking of obstacles and abruptly changed terrain
Solution Approach 1:
The patent divides the tracking task among multiple UAVs, each responsible for specific spatial zones or aspects of target observation. This segmentation allows the system to maintain continuous tracking by distributing the observational burden across multiple agents, ensuring that if one UAV loses sight of the target due to obstacles, other UAVs can maintain the tracking link.
Solution Approach 2:
The patent transitions from single-UAV two-dimensional planar tracking to multi-UAV three-dimensional spatial tracking. By deploying UAVs at different positions and altitudes, the system creates multiple observation angles and dimensions, enabling continuous target acquisition even when line-of-sight is blocked from any single viewpoint. This dimensional expansion resolves the reliability issue in complex environments.
3Measurement precision
If multi-source fusion sensors are deployed on UAV cluster, then the target feature information extraction quality is improved, but the device complexity and sensing coordination difficulty increase
Solution Approach 1:
The patent introduces a centralized data fusion center that acts as an intermediary between multiple UAVs and their diverse sensors. This fusion center receives raw data from millimeter wave radars, infrared sensors, and visual sensors across the UAV cluster, performs synchronized processing, and generates unified target state estimates. The intermediary architecture manages the complexity of multi-source fusion by centralizing the computationally intensive integration tasks while allowing individual UAVs to operate with simpler onboard processing.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Improves target feature information extraction quality and reduces sensing and communication losses, enhancing tracking performance in low luminance conditions.
Implementation Method 1
performing multi-source environment sensing by using sensors that include a millimeter wave radar sensor
Implementation Method 2
an infrared visual sensor... calculating world coordinates of a tracked target
Data Source
AI summary
The present disclosure provides a multi-view collaborative tracking method and apparatus for a fast moving target under a low luminance condition, including a multi-view target tracking algorithm based on radar-vision fusion data and an unmanned aerial vehicle (UAV) cluster collaborative tracking technology. This method takes the spatiotemporal normalized multi-source data as the learning objective of multi-agent reinforcement learning, to drive the cluster to collect data from multiple angles and directions. In this way, highly-efficient multi-angle collaborative observation can be performed on a high-speed moving object in an environment of weak texture and low luminance.