Fatality Probability Grid Optimization for Rogue Missile Safety
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Solution Overview
Problem
Existing methods face challenges in accurately calculating fatality probability distributions for rogue missiles with atypical trajectories, particularly in sparse fall regions due to sensitivity to grid size and unreliable extrapolation of far tails, where the distribution type is often unknown.
Innovation Solution
The method involves iteratively optimizing local grid size to satisfy statistical constraints, specifically by updating grid cell sizes until a predefined minimal fatality probability threshold is reached, and using confidence corrections to ensure accurate fatality probability evaluation, even in sparse fall areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Monte-Carlo simulations are performed with discrete grid cells to map falls density, then the falls distribution can be visualized and analyzed, but the fatality probability evaluation becomes highly sensitive to grid cell size in sparse falls areas
Solution Approach 1:
The patent implements dynamic grid cell size adjustment based on local falls density characteristics. The grid resolution is automatically adapted to match the statistical confidence requirements of each region, allowing fine resolution in dense areas and coarser resolution in sparse areas while maintaining overall evaluation reliability.
Solution Approach 2:
The patent changes the grid cell size parameter dynamically across different spatial regions. By adjusting this fundamental parameter based on local falls density, the system optimizes the balance between measurement precision and reliability, ensuring that fatality probability evaluations are not unduly influenced by arbitrary grid size choices.
2Area of stationary object
If extrapolation methods are used to determine far tails of falls distribution, then coverage of sparse falls regions can be extended, but the results become unreliable due to unknown distribution types
Solution Approach 1:
The patent employs feedback mechanisms where the simulation results from Monte-Carlo runs are continuously analyzed to refine the falls distribution model. This iterative process allows the system to learn the actual distribution characteristics from the simulation data itself, rather than relying on predetermined extrapolation assumptions, thereby improving reliability in sparse regions.
Solution Approach 2:
The patent performs preliminary Monte-Carlo simulations to establish the falls distribution pattern before making predictions in sparse regions. By gathering sufficient simulation data first, the system can better characterize the distribution type and reduce uncertainty in extrapolation, improving the reliability of far tail predictions.
3Ease of manufacture
If a fixed grid structure is used for falls mapping, then the implementation is simple and consistent, but the grid cell size cannot be optimized for different regions of interest
Solution Approach 1:
The patent segments the falls mapping area into multiple zones with different grid cell sizes based on local requirements. This segmentation allows each region to have optimized resolution - finer grids in areas of interest and coarser grids in less critical areas - while maintaining overall system simplicity through automated zone assignment.
Solution Approach 2:
The patent applies local quality by assigning different grid cell sizes to different spatial regions based on their specific characteristics. Areas of interest receive finer resolution for precise fatality probability measurement, while other regions use coarser grids, optimizing measurement precision without uniformly increasing complexity across the entire system.
4Reliability
If the number of Monte-Carlo simulation runs is increased to improve statistical confidence in sparse falls areas, then the fatality probability evaluation becomes more reliable, but the computational time and resources increase
Solution Approach 1:
The patent applies partial action by concentrating simulation runs in regions where they provide the most value - specifically in sparse falls areas that are critical for safety assessment. Rather than uniformly increasing simulation runs across all regions, the system targets computational resources to areas where they most improve reliability, achieving better statistical confidence with fewer total runs.
Solution Approach 2:
The patent dynamically adjusts the number of simulation runs based on the statistical confidence requirements of each region. Sparse falls areas that require higher confidence levels receive more simulation runs, while dense areas with already high confidence require fewer runs, optimizing the balance between reliability and computational time.
Data Source
AI summary
A method for creating a dedicated optimal local grid around a place of interest comprises: a) iteratively updating the local grid size such as to satisfy statistical constraints; and b) discontinuing the iterative process of step (a) when a predefined threshold of said statistical constraint is reached.