A distributed probability preserving estimation method for wireless sensor networks under a deception attack

By establishing a mathematical model of a nonlinear system and designing an adaptive update strategy for the weight matrix of a neural network, the problem of state estimation in wireless sensor networks under deception attacks is solved, achieving more realistic and flexible estimation of system state and information.

CN116992927BActive Publication Date: 2025-12-23NANJING UNIV OF SCI & TECH
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Patent Information

Application Number
CN202210395866.1
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-04-15
Publication Date
2025-12-23
Estimated Expiration
2042-04-15

AI Technical Summary

Technical Problem

When faced with spoofing attacks, the performance of existing technologies such as Kalman filtering and H∞ methods is limited by the Gaussian assumption and the energy bounded assumption, and they cannot effectively deal with nonlinear spoof signals in practical engineering, resulting in poor state estimation performance in wireless sensor networks.

Method used

A mathematical model of the nonlinear system is established, and a distributed state estimator structure based on a neural network is designed. By adaptively updating the neural network weight matrix and designing the distributed probability-preserving state estimator gain, the estimator parameters are optimized to achieve local optimal estimation performance.

Benefits of technology

It provides a more realistic approach to engineering applications, enabling estimation of system state and attack innovations within a limited timeframe, avoiding unnecessary stringent constraints, and offering greater flexibility and expected performance.

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Abstract

The application discloses a wireless sensor network distributed guaranteed probability estimation method under a deception attack, and specifically comprises the following steps: a nonlinear system mathematical model under the condition that innovation is deceived is established; a neural network-based distributed state estimator structure model is established according to the nonlinear system mathematical model under the condition that innovation is deceived; a neural network weight matrix adaptive updating strategy is designed; a distributed guaranteed probability state estimator gain is designed; and estimator parameters are optimized to achieve local optimal estimation performance; the application comprehensively considers the nonlinear characteristics and the influence of malicious attacks existing in actual engineering, can be widely applied to military monitoring systems and environmental monitoring systems, estimates the state of a monitoring or monitoring object, is more in line with engineering application, and is high in practicality.
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