Cascade Kalman Filter for Projectile Origin Accuracy
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Solution Overview
Problem
Current point of origin estimates for projectiles, such as mortar shells, remain error-prone and inaccurate, hindering effective counter-attack capabilities despite the use of Kalman filter-based algorithms.
Innovation Solution
A method involving a cascade of two Kalman filters, with initial and secondary calculations, including discriminant analysis and specific drag models, to enhance the accuracy of projectile point of origin estimation, utilizing radar data and environmental factors for refined trajectory prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single Kalman filter-based algorithm is used to estimate point of origin, then the system remains simple and computationally efficient, but the accuracy of point of origin estimates remains error-prone and insufficient
Solution Approach 1:
The patent divides the point of origin estimation process into multiple sequential stages using a cascade of Kalman filters. Each filter performs a specific function (e.g., initial state estimation, trajectory refinement, point of origin calculation) to progressively improve accuracy. This segmentation allows the system to achieve high measurement precision while maintaining manageable algorithmic complexity through modular design.
Solution Approach 2:
The patent applies preliminary actions by performing initial target detection, classification, and state estimation before the final point of origin calculation. The cascade structure processes data through multiple preparatory filtering stages that refine the input data quality, enabling more accurate final estimates while organizing complexity into manageable sequential steps.
2Measurement precision
If more sophisticated algorithms are implemented to improve point of origin accuracy, then measurement precision increases, but computational time and processing complexity increase
Solution Approach 1:
By segmenting the computational process into multiple specialized Kalman filter stages, each performing a focused calculation task, the system achieves high precision without requiring a single monolithic complex algorithm. This modular approach optimizes computational efficiency while maintaining accuracy.
Solution Approach 2:
The patent employs parameter changes by adapting the Kalman filter models to incorporate specific drag coefficients and ballistic characteristics differentiating mortar shells from other projectiles. This allows the system to improve measurement precision for specific target types through parameter optimization rather than increasing overall algorithmic complexity.
Data Source
AI summary
A method of using a discriminant analysis and Kalman filter cascade to improve the accuracy of point of origin solutions. Tracking information about a potential target is utilized by an initial discrimination function to classify the target as a projectile. Using that information, the output of a first Kalman filter is fed into an additional discrimination function to further classify the type or sub-class of the projectile. A second Kalman filter can employ type-specific information to obtain a point of origin solution with increased efficiency and accuracy.