Weapon System Hit Probability Indicator Calculation
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Solution Overview
Problem
Existing weapon systems face challenges in optimizing effector performance due to inconsistencies in indicator displays and rigid coupling between performance data and indicators, leading to reduced flexibility in development and maintenance, especially under limited real-time resources.
Innovation Solution
A method using artificial neural networks to determine hit probability indicators by reducing a global representation of scenario parameters, allowing for real-time processing and decoupling effector-dependent variables from indicator calculations, enabling flexible system modifications and maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If mutually independent representation functions (e.g., polynomials) are used to map individual indicators, then real-time calculation is enabled, but inconsistencies between displayed indicators occur due to limited approximation accuracy
Solution Approach 1:
The patent merges multiple independent indicator representations into a single unified global performance representation that models the entire weapon system performance. This global representation is then reduced to individual indicator representations, ensuring consistency across all indicators while enabling real-time calculation through the unified model.
2Productivity
If performance data are rigidly coupled to corresponding indicators through independent representation functions, then direct calculation is possible, but flexibility in development and maintenance is reduced
Solution Approach 1:
The patent segments the performance data processing into two distinct layers: a global performance representation that is effector-independent and can be maintained separately, and indicator-specific representations that are derived from the global model. This segmentation allows independent modification of indicators without changing the core performance data, enhancing flexibility in development and maintenance.
3Reliability
If a global representation of the weapon system is used, then consistency between indicators is improved, but real-time processing capability is reduced due to computational complexity
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing the global performance representation offline, where computationally intensive modeling can be performed without real-time constraints. The stored global representation is then reduced to indicator-specific representations that can be evaluated efficiently in real-time during weapon system operation, balancing consistency and processing speed.
Data Source
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AI summary
The method involves identifying the scenario parameters (36) of a weapon system, and reducing a global representation (42) of the weapon system to a representation (46a,46b,46c,46d), adapted to an indicator (24a,24b,24c,24d), based on the determined scenario parameters. The indicator is determined based on the reduced representation. A position or location of weapon system is provided in the scenario parameters relative to a target to be taken. The global representation approximates a branch of a parameter space from the scenario parameters. Independent claims are also included for the following: (1) a method for determining a representation of weapon system; (2) a computer program stored on a computer-readable medium for determining an indicator for the striking probability of a weapon system; and (3) a weapon system with sensors for determining the scenario parameters.