Guidance Controls for Non-Gaussian Target Tracking
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Solution Overview
Problem
Guidance systems face challenges in navigating a pursuer to a target with non-Gaussian statistics, as existing methods struggle to provide optimal control strategies that minimize miss distance and pursuer maneuvering costs while adhering to state constraints, especially when the target location cannot be described using Gaussian probability distributions.
Innovation Solution
A method that involves generating candidate guidance controls, evaluating their costs based on projected pursuer trajectories and miss distances, and iteratively refining these controls using an optimization algorithm, such as a swarm algorithm, to determine the optimal guidance control for navigating the pursuer to a target with non-Gaussian statistics, considering pursuer state constraints and termination conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Particle Filtering is used to represent non-Gaussian target probability distribution, then target location estimation capability is improved, but guidance control optimization becomes more difficult
Solution Approach 1:
The guidance problem is segmented into two independent parts: (1) target state estimation using Particle Filter, and (2) guidance control optimization using swarm algorithms. This segmentation allows each sub-problem to be solved independently with appropriate methods, resolving the complexity issue while maintaining estimation precision.
Solution Approach 2:
The invention introduces an intermediary cost function that bridges the particle filter output and the guidance control optimization. The cost function incorporates both miss distance and control effort, allowing the non-Gaussian target distribution to be translated into a form suitable for swarm-based optimization without direct coupling between the estimation and control modules.
2Manufacturing precision
If optimal guidance control is determined by minimizing cost function, then miss distance is reduced, but computational complexity increases
Solution Approach 1:
The invention replaces traditional analytical or numerical optimization methods with swarm-based optimization algorithms. This substitution allows for parallel computation of multiple candidate controls, reducing the computational burden of finding the optimal control that minimizes the cost function while achieving minimal miss distance.
Solution Approach 2:
The swarm algorithm evaluates multiple candidate controls simultaneously, using a population-based approach where not all candidates need to be perfectly optimized. This partial action approach allows the system to find sufficiently good solutions with reduced computational complexity compared to exhaustive optimization methods.
3Use of energy by moving object
If guidance law minimizes control effort, then maneuvering cost is reduced, but miss distance may increase
Solution Approach 1:
The cost function uses parameter weighting to balance control effort and miss distance. By adjusting the relative weights of these parameters in the cost function, the system can optimize the trade-off between maneuvering cost and accuracy, finding the optimal guidance control that achieves acceptable miss distance with minimal control effort.
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AI summary
A method of guiding a pursuer to a target is provided, and is of particular use when the possible target location is described by non-Gaussian statistics. Importantly, the method takes into account the fact that different potential target tracks in the future have significantly different times to go. That can give rise to emergent behaviour, in which the guidance method covers several possible outcomes at the same time in an optimal way. An example embodiment of the method combines Particle Filter ideas with Swarm Optimization techniques to form a method for generating guidance commands for systems with non- Gaussian statistics. That example method is then applied to a dynamic mission planning example, to guide an airborne pursuer to a ground target travelling on a network of roads where the pursuer has no-go areas, to avoid collateral damage.