Surrogate Model for Weapon Mission Planning
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Solution Overview
Problem
Existing mission planning systems for weapons systems face challenges in accurately and efficiently determining weapon performance characteristics during combat operations due to the computational intensity of detailed kinematic models, which are often unsuitable for deployment in field settings with limited computing power, and simplifying these models can compromise accuracy.
Innovation Solution
The use of Gaussian Process (GP) or Neural Network-based surrogate models to provide a functional approximation of weapon performance characteristics, requiring less processing power and storage, allowing for rapid and accurate calculations of performance characteristics such as Launch Success Zone and Launch Acceptability Regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a detailed kinematic model is used to accurately model weapon behavior, then measurement precision is improved, but device complexity and computing power requirements increase
Solution Approach 1:
The patent creates a simplified copy (surrogate model) of the complex kinematic model. This surrogate model replicates the essential predictive capabilities of the detailed model but with reduced computational complexity, making it suitable for deployment in field weapons systems with limited processing power while maintaining sufficient accuracy for operational decision-making
Solution Approach 2:
The invention transforms the complex kinematic model into a surrogate model by changing the parameters and structure of the computational system. The surrogate model uses a different set of parameters and mathematical relationships that achieve comparable predictive accuracy with significantly lower computational requirements, resolving the contradiction between precision and complexity
2Measurement precision
If a detailed kinematic model is used, then measurement precision is improved, but productivity is worsened due to time-consuming calculations
Solution Approach 1:
The surrogate model serves as a computational copy that preserves the accuracy function of the detailed kinematic model while eliminating the time-consuming calculation burden. This enables rapid assessment of weapon capability in near real-time, supporting fast operational decision-making without sacrificing predictive accuracy
Solution Approach 2:
The surrogate model is developed and validated in advance during the design phase, capturing the essential behavior of the weapon system beforehand. This preliminary creation of the simplified model allows for rapid predictions during actual combat operations without needing to run complex calculations in real-time
3Device complexity
If the kinematic model is simplified to reduce computing power requirements, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The surrogate model is constructed as a faithful copy of the detailed kinematic model's predictive behavior, not a crude simplification. It preserves the essential relationships and accuracy characteristics of the original model while being implemented in a computationally efficient form suitable for field deployment
Solution Approach 2:
The invention changes the parameter representation and mathematical structure from a complex detailed model to a simplified surrogate model with transformed parameters. This parameter transformation maintains measurement precision while reducing computational complexity, enabling accurate predictions in resource-constrained environments
4Measurement precision
If a look up table is used to store weapon capability information, then measurement precision is maintained, but device complexity and storage requirements increase significantly
Solution Approach 1:
Instead of storing comprehensive look up tables with extensive parameter combinations, the surrogate model transforms the approach by using a compact mathematical representation. This parameter transformation enables the system to calculate weapon capability on-demand with high accuracy while requiring minimal storage space and computational resources
Data Source
AI summary
A mission planning method for use with a weapon is disclosed. The method comprises: obtaining a first training data set describing the performance of the weapon; using the first training data set and a Gaussian Process (GP) or Neural Network to obtain a first surrogate model giving a functional approximation of the performance of the weapon; and providing the first surrogate model to a weapons system for use in calculating a performance characteristic of the weapon during combat operations.