Dynamic Particle Allocation for State Estimation Stability
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Solution Overview
Problem
In large-scale systems like digital twins, existing particle filter techniques struggle to improve stability and accuracy of state estimation, particularly when allocating particles to difficult-to-track components, leading to reduced estimation performance.
Innovation Solution
An information processing system that forms multiple particle filters, determines the risk of each system's state, and dynamically allocates a minimum number of particles to all systems, with a higher number allocated to systems with higher risk, ensuring stability and accuracy while maintaining within a preset total particle count.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the number of particles is reduced for parts difficult to track, then the allocation efficiency is improved, but the estimation stability and accuracy deteriorate
Solution Approach 1:
The patent implements dynamic particle allocation where the number of particles assigned to each system is adjusted in real-time based on tracking difficulty and risk assessment. Systems with higher tracking difficulty or risk are dynamically allocated more particles, while those with lower risk receive fewer particles. This dynamic adjustment mechanism resolves the contradiction by adapting particle allocation to actual system needs rather than using static allocation, thereby maintaining estimation stability for difficult-to-track systems while improving overall allocation efficiency.
2Measurement precision
If more particles are allocated to difficult-to-track parts, then the estimation accuracy is improved, but the computational cost increases
Solution Approach 1:
The patent applies local quality by allocating different numbers of particles to different systems based on their individual tracking difficulty and risk characteristics. Instead of uniformly distributing particles across all systems, the invention assesses each system's specific needs and allocates particles locally according to those needs. This approach improves estimation accuracy for difficult-to-track systems without unnecessarily increasing computational cost for easier-to-track systems, thereby resolving the contradiction between accuracy and computational cost.
Solution Approach 2:
The patent changes the parameter of particle allocation based on risk assessment and tracking difficulty metrics. By introducing risk as a parameter and adjusting particle allocation according to this parameter, the system optimizes the balance between estimation accuracy and computational cost. Systems with higher risk parameters receive more particles to maintain accuracy, while systems with lower risk parameters receive fewer particles to reduce computational overhead.
3Device complexity
If a fixed number of particles is allocated to each system, then the system complexity is reduced, but the estimation performance for multiple systems deteriorates
Solution Approach 1:
The patent transitions from static fixed particle allocation to dynamic risk-based allocation. The system continuously assesses the risk level of each system and adjusts particle allocation accordingly. This dynamic approach maintains relatively simple system architecture while significantly improving estimation performance for multiple systems with varying tracking difficulties, thereby resolving the contradiction between system complexity and estimation performance.
Data Source
AI summary
An information processing system 100 according to the present disclosure is an information processing system that forms a plurality of particle filters for estimating states of a plurality of systems. The information processing system includes a determination unit 121 configured to determine a risk of the state of each of the systems based on the state of each of the systems, and a calculation unit 122 configured to calculate the number of particles to be allocated to each of the systems based on the risk and also calculate the number of particles so as to allocate at least a preset minimum number of particles to all the systems.