Secure Localization for Multi-Robot Systems Under Deception Attacks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing estimation methods for multi-mobile robots systems fail to simultaneously handle state estimation with random coupling strength and random sensor one-step delay under deception attacks, leading to low estimation accuracy.
Innovation Solution
A secure localization method for multi-mobile robots based on network communication is proposed, which involves establishing a nonlinear dynamic model, designing a secure estimator, calculating an upper bound on the one-step prediction error covariance matrix, and determining an estimator gain matrix to improve estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing estimation methods are used for multi-mobile robots systems, then the system can operate with simple estimation algorithms, but the estimation accuracy is low when dealing with random coupling strength and random sensor one-step delay under deception attacks
Solution Approach 1:
The patent segments the estimation problem into two separate filters: a state filter for normal operation and a secure filter for deception attack detection. Each filter processes different aspects of the system state independently, allowing the complex estimation problem to be divided into manageable parts that can handle random coupling strength and sensor delays separately, thereby improving estimation accuracy without overwhelming computational complexity
Solution Approach 2:
The patent introduces an intermediary mechanism by designing a secure filter that acts as a mediator between the state filter and the deception attack detection system. This secure filter receives measurements from both filters and determines whether deception attacks are present, then selects appropriate estimation results. This intermediary structure enables the system to handle complex attack scenarios while maintaining reasonable algorithmic complexity
2Measurement precision
If a secure estimator is designed to handle deception attacks with random coupling strength and random sensor one-step delay, then the estimation accuracy improves, but the computational cost increases
Solution Approach 1:
The patent employs dynamic filtering strategies where the secure filter adaptively switches between using the state filter results and its own secure estimation results based on deception attack detection. This dynamic approach allows the system to maintain high estimation accuracy during attacks while reducing computational energy consumption during normal operation by relying on the simpler state filter
Solution Approach 2:
The patent changes the operational parameters of the estimation system by introducing a deception attack detection mechanism that dynamically adjusts which filter results are trusted. When deception attacks are detected, the system switches to using the secure filter's estimation results; otherwise, it uses the state filter results. This parameter switching mechanism maintains accuracy under attacks while minimizing computational energy consumption during normal operation
Data Source
AI summary
A secure localization method for multi-mobile robots system based on network communication includes: Step 1, establishing a nonlinear dynamic model of a multi-mobile robots system based on network communication; Step 2, designing a secure estimator for the nonlinear dynamic model; Step 3, calculating an upper bound on a one-step prediction error covariance matrix Σi,k+1|k for each mobile robot in the network communication; Step 4, based on Σi,k+1|k, calculating an estimator gain matrix Ki,k+1 for each mobile robot in the network communication; Step 5, substituting the estimator gain matrix Ki,k+1 calculated in Step 4 into Step 2 to obtain a state estimation {circumflex over (x)}i,k+1|k+1 at time k+1; determining whether k+1 reaches a total duration M, that is, if k+1<M, performing Step 6, and if k+1=M, ending; and Step 6, based on Ki,k+1, calculating an upper bound on an estimation error covariance matrix Σi,k+1|k+1 of each mobile robot; let k=k+1, and performing Step 2 until k+1=M is satisfied.