Passive Camera Array Tracking of Bird and Drone Swarms
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems fail to reliably detect and track small flying objects like birds and drones at ranges relevant for aircraft collision avoidance due to insufficient resolution and background clutter, making collision detection unreliable.
Innovation Solution
The implementation of area-scanning optical systems using cameras to capture and process images of a volume of space in front of an aircraft, enabling swarm angle tracking by detecting changes in pixel values indicative of swarm motion, even at low feature-to-noise ratios and through difficult background clutter, without the need for active sensors or licensing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If camera systems are used to detect small flying objects, then the system is completely passive and eliminates interference concerns, but the resolution is insufficient for detection and tracking at ranges of interest
Solution Approach 1:
The patent merges multiple camera systems to form an array configuration, combining their individual detection capabilities into a unified system that achieves higher effective resolution through spatial distribution and signal integration across multiple sensors
Solution Approach 2:
The patent transitions from single-camera two-dimensional imaging to multi-camera three-dimensional spatial sampling, adding the dimension of baseline separation between cameras to resolve the resolution-range tradeoff through stereoscopic and angular measurement capabilities
2Device complexity
If single camera tracking is used, then the system is simpler, but tracking reliability is insufficient for objects less than a pixel in size
Solution Approach 1:
The patent combines data from multiple cameras to track swarms as collective entities, merging individual camera observations into unified swarm position and velocity estimates that are more reliable than single-camera tracking of individual sub-pixel objects
3Measurement precision
If stereoscopic camera systems are used, then range information is obtained, but the system complexity increases and licensing requirements apply
Solution Approach 1:
The patent implements partial stereoscopic functionality by using multiple cameras primarily for angular tracking of swarms rather than full three-dimensional tracking of individual objects, obtaining sufficient range information through angular measurements and time-of-flight calculations without requiring complete stereoscopic processing of all objects
4Measurement precision
If traditional tracking methods are used, then individual object tracks are formed, but tracking becomes unreliable when objects are less than a pixel in size
Solution Approach 1:
The patent merges individual object tracking into swarm-level tracking, combining observations of multiple sub-pixel objects into collective swarm position, velocity, and trajectory estimates that are statistically more reliable than attempting to track individual unresolved objects separately
Solution Approach 2:
The patent creates a simplified representation or 'copy' of the swarm as a single tracked entity with aggregated properties (center position, velocity, size) rather than attempting to maintain individual tracks of all constituent objects, enabling reliable tracking at the swarm level when individual object resolution is insufficient
Data Source
AI summary
Systems and methods for tracking swarms of flying objects for collision avoidance using area-scanning optical systems. Images of the volume of space forward of an aircraft in flight are captured using one or more cameras and then the images are processed to determine whether the image data indicates the presence of a swarm. The camera-based collision avoidance system is configured to detect and angularly track small objects flying in a swarm at sufficiently far ranges to cue or alert the flight control system onboard an autonomous or piloted aircraft to avoid the swarm. A swarm angle tracking algorithm is used to recognize swarms of flying objects that move in unison in a certain direction by detecting a consistent characteristic of the pixel values in captured images which is indicative of swarm motion.