Target Classification System Using Ray Tracing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems for accurately locating and classifying targets in military or emergency response operations are cumbersome, time-consuming, and prone to errors, especially in stressful situations, due to reliance on manual methods, limited operational ranges, and vulnerability to jamming or detection.
Innovation Solution
A target classification system that includes a user device with a visual alignment aid, pose sensor, and processor to determine the pose of the line of sight, allowing users to input target classifications via buttons or voice, and outputs targeting data including coordinates and classifications, utilizing edge computing for faster response and reduced latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual surveying and classification methods are used, then system complexity is reduced, but measurement precision and productivity deteriorate
Solution Approach 1:
The in-field device integrates multiple functions including camera imaging, pose sensing, ray tracing computation, and target classification into a single unified system. This multi-functional integration enables precise target location determination while consolidating system complexity into one device rather than requiring separate manual operations for each function.
Solution Approach 2:
The system replaces manual mechanical surveying methods with automated computational processes. The processor automatically performs ray tracing calculations using camera pose data and screen space coordinates to determine world space target locations, eliminating the need for manual coordinate measurements and calculations.
2Productivity
If automated target classification is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The system enables operators to perform classifications themselves using the integrated device, eliminating the need for separate classification processes. The device provides all necessary functions including image capture, coordinate transformation, and classification interfaces in one self-contained unit that serves the operator's complete target identification needs.
3Measurement precision
If multiple data sources are integrated, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The system merges multiple data sources including camera images, pose sensor data, and user inputs into a single integrated workflow. The processor automatically combines these diverse data types through ray tracing computations to produce unified target location results, eliminating the need for operators to manually integrate separate data sets.
Solution Approach 2:
The ray tracing process acts as an intermediary computational mechanism that automatically transforms data from different coordinate systems (screen space to world space) and integrates multiple input sources. This intermediary computation handles the complexity of data integration transparently, presenting simplified results to the operator.
4Productivity
If rapid target classification is achieved, then productivity is improved, but measurement precision may deteriorate
Solution Approach 1:
The system performs preliminary computations including camera pose determination and coordinate system transformations automatically and rapidly. These preliminary actions prepare the data structure and coordinate frameworks in advance, enabling quick classification decisions without compromising the precision of the underlying spatial calculations.
Data Source
AI summary
One example provides a target classification system comprising a display subsystem configured to display an image captured by a camera of an in-field device. The image includes one or more targets. The target classification system is configured to receive a user input indicating a location of the one or more targets in a screen space coordinate system of the display subsystem. Location information in a world space coordinate system is determined by receiving a pose of the camera; using the pose of the camera and the location in the screen space to trace a ray; and using at least a position of the camera and an orientation of the ray to generate coordinates in the world space. Target classification information is determined, and targeting data is output comprising the coordinates in the world space and the target classification information.