Threat Object Map Creation Using 3D Sphericity Metric
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Solution Overview
Problem
Current methods for creating threat object maps from data received from multiple sensors are unsatisfactory due to sensor biases, spurious detections, and position errors, leading to incorrect target identification in missile systems and similar applications.
Innovation Solution
A system that uses a three-dimensional sphericity metric to create a threat object map by determining correlation maps between detection data from multiple sensors, selecting the correlation map with the greatest mean sphericity, and targeting the desired object based on this map.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data from multiple sensors is used to create a threat object map, then the coverage and detection capability are improved, but the accuracy deteriorates due to sensor biases, spurious detections, and position errors
Solution Approach 1:
The patent segments the sensor data processing by dividing the set of spatial coordinates into multiple subsets, where each subset is used to generate a separate correlation map. This segmentation allows the system to evaluate multiple hypotheses about target identity independently, then select the most reliable correlation map based on sphericity metrics, thereby resolving the contradiction between using multiple sensors and maintaining accuracy.
Solution Approach 2:
The patent changes the evaluation parameter from direct coordinate matching to sphericity-based correlation map evaluation. By computing sphericity values for different correlation maps and selecting the one with the greatest mean sphericity, the system transforms the problem from comparing raw coordinate data (which is affected by sensor errors) to evaluating geometric properties that are more robust to such errors.
2Reliability
If multiple correlation maps are generated from different four-point subsets, then the robustness against spurious detections is improved, but the computational complexity increases
Solution Approach 1:
The patent applies partial action by selecting a representative number of four-point subsets from the overall set of spatial coordinates rather than processing all possible combinations. This selective approach provides sufficient robustness against spurious detections while avoiding the combinatorial explosion that would result from exhaustive processing of all possible subsets.
Solution Approach 2:
The patent replaces complex mechanical-like coordinate matching processes with a mathematical sphericity evaluation system. Instead of directly comparing spatial coordinates from different sensors (which requires handling multiple error sources), the system uses sphericity metrics to evaluate correlation maps, substituting geometric computation for more complex data reconciliation processes.
Data Source
AI summary
In order to target and intercept a desired object within a number of objects detected in an environment, detection data is received from two different sensors, where the detection data includes spatial coordinates. A set of four-point subsets (tetrahedra) are selected from each set of spatial coordinates. A number of correlation maps are determined between the first set of spatial coordinates and the second set of spatial coordinates based on the plurality of four-point subsets. The mean sphericity for each corresponding plurality of four-point subsets in the plurality of correlation maps is determined, and a threat object map based on the correlation map having the greatest mean sphericity is created. The desired object is targeted based on the correlation map.