UAV Multi-Target Tracking with Primary-Target Group Control
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Solution Overview
Problem
Existing technologies face challenges in efficiently tracking multiple targets using unmanned aerial vehicles (UAVs) equipped with imaging devices, especially when targets and UAVs are moving in complex environments.
Innovation Solution
A computer-implemented method and system for tracking multiple targets by identifying targets based on images from a UAV's imaging device, determining a target group, and controlling the UAV to track the target group, utilizing techniques such as target state estimation and feedback control loops.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple targets are tracked simultaneously using a UAV with imaging devices, then the surveillance coverage and reconnaissance capability are improved, but the system complexity and computational load increase significantly
Solution Approach 1:
The patent segments the multiple target tracking problem into independent single-target tracking modules. Each target is processed by dedicated tracking algorithms (e.g., Kalman filter, particle filter) that operate independently, allowing the system to handle multiple targets without proportionally increasing overall system complexity. The segmentation is achieved through target detection and association mechanisms that divide the surveillance space into discrete target zones.
Solution Approach 2:
The patent implements universal tracking algorithms that can handle various target types (ground vehicles, aerial objects, maritime targets) and diverse surveillance scenarios using the same core system architecture. The imaging device and processing system are designed to be multi-functional, adapting to different target characteristics through parameter adjustment rather than requiring separate specialized systems for each target type.
2Measurement precision
If the UAV adjusts its position and orientation to maintain accurate tracking of moving targets, then the tracking precision is improved, but the energy consumption and operational time are reduced
Solution Approach 1:
The patent implements periodic tracking updates where the UAV adjusts its position and orientation at optimized intervals rather than continuously. The tracking system uses predictive algorithms (Kalman filter, particle filter) to estimate target positions between updates, allowing the UAV to maintain tracking precision while reducing the frequency of energy-consuming maneuvers. The update period is dynamically adjusted based on target speed, distance, and mission requirements.
Solution Approach 2:
The patent uses predictive tracking algorithms that estimate future target positions based on historical motion data before the UAV needs to reposition. By calculating predicted target locations in advance, the system can plan more efficient flight paths that minimize energy consumption while maintaining tracking accuracy. The preliminary action is achieved through motion prediction models that anticipate target behavior.
3Loss of energy
If the UAV maintains a stable position to reduce energy consumption, then the energy efficiency is improved, but the ability to track fast-moving targets deteriorates
Solution Approach 1:
The patent introduces computational algorithms (Kalman filter, particle filter, association mechanisms) as intermediaries between the stationary UAV and the moving targets. These intermediaries process imaging data to calculate target positions, velocities, and trajectories, allowing the UAV to remain relatively stable while still tracking fast-moving targets through software-based motion compensation rather than physical repositioning.
Solution Approach 2:
The patent replaces mechanical UAV repositioning with computational image processing and algorithmic tracking. Instead of physically moving the UAV to follow fast-moving targets, the system uses digital signal processing, feature tracking, and predictive algorithms to maintain target acquisition. This substitution of mechanical action with computational methods enables energy-efficient tracking of high-speed targets.
Data Source
AI summary
A computer-implemented method for tracking multiple targets includes identifying a primary target from a plurality of targets based on a plurality of images obtained from an imaging device carried by an aerial vehicle via a carrier, determining a target group including one or more targets from the plurality of targets, where the primary target is always in the target group. Determining the target group includes determining one or more remaining targets in the target group based on a spatial relationship or a relative distance between the primary target and each target of the plurality of targets other than the primary target. The method further includes controlling at least one of the aerial vehicle or the carrier to track the target group as a whole.