Distributed Fusion Filtering for Multi-Rate Systems Under Eavesdropping
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Solution Overview
Problem
Existing distributed fusion filtering methods struggle to concurrently address the filtering issues for multi-rate nonlinear systems with fading measurements and eavesdropping, leading to diminished filtering accuracy.
Innovation Solution
An anti-eavesdropping distributed fusion filtering method is developed, which includes establishing a dynamic model for multi-rate nonlinear systems, transforming it into a single-rate system, designing an anti-eavesdropping distributed fusion filter, and introducing artificial noise to prevent data interception, while using a prediction compensation strategy and local distributed filters to enhance security and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If distributed fusion filtering is applied to multi-rate nonlinear systems, then filtering accuracy is improved, but vulnerability to eavesdropping increases
Solution Approach 1:
The patent converts the harmful effect of eavesdropping into a beneficial security mechanism by introducing artificial noise. The added noise disrupts potential eavesdropping attempts while the legitimate filtering operation continues normally, transforming a security vulnerability into an active protection mechanism
Solution Approach 2:
Artificial noise serves as an intermediary element between the filtering system and potential eavesdroppers. This noise component mediates the communication channel, protecting the integrity of the filtering data without interfering with the legitimate multi-rate nonlinear system operation
2Reliability
If artificial noise is added to prevent eavesdropping, then security is improved, but filtering accuracy deteriorates
Solution Approach 1:
The patent applies local quality by adding artificial noise selectively to specific communication channels between sensor nodes rather than uniformly across all data streams. This targeted approach protects vulnerable channels while preserving filtering accuracy in channels where eavesdropping is less likely
Solution Approach 2:
The patent dynamically adjusts the noise parameters (amplitude, frequency, distribution) based on the operating conditions and security requirements. By changing noise parameters adaptively, the system maintains security protection while minimizing the negative impact on filtering accuracy under different operational scenarios
3Adaptability or versatility
If multi-rate nonlinear system filtering is implemented, then system adaptability is improved, but computational complexity increases
Solution Approach 1:
The patent segments the complex multi-rate nonlinear filtering problem into smaller, manageable sub-problems that can be solved independently by individual sensor nodes. Each node processes its local data separately, and results are fused distributedly, reducing the overall computational burden compared to centralized processing
Solution Approach 2:
The patent employs dynamic filtering parameters and adaptive update rates that adjust according to system conditions. This dynamic approach allows the filter to maintain high adaptability to changing system characteristics while reducing computational complexity during stable operating conditions where full-rate processing is unnecessary
Data Source
AI summary
An anti-eavesdropping distributed fusion filtering method for the multi-rate nonlinear system over sensor network includes: Step 1, establishing a dynamic model for the multi-rate nonlinear system over sensor network; Step 2, transforming the multi-rate nonlinear system dynamic model into a single-rate nonlinear system dynamic model through the prediction compensation strategy; Step 3, designing an anti-eavesdropping distributed fusion filter; Step 4, calculating an upper bound on the one-step prediction error covariance (tk+1|tk); Step 5, deriving the local distributed filter parameter Ki(tk+1); Step 6, deriving the selection matrix Lij(tk+1); Step 7, substituting Ki(tk+1) and Lij(tk+1) into Step 3 to obtain the fusion filter {circumflex over (x)}CI(tk+1|+tk+1); Step 8, solving for the upper bound on the local filtering error covariance (tk+1|tk+1). The method solves the problem that the existing fusion filtering method cannot simultaneously deal with the filtering problem for multi-rate nonlinear systems with eavesdroppers and fading measurements, thereby improving the accuracy of the filtering performance.