Event-Triggered Resilient Filtering for 2-D Systems with Asynchronous-Delay: Handling Binary Encoding-Decoding with Probabilistic Bit Flips

Core Concepts

This paper proposes a new event-triggered resilient filtering approach for a class of two-dimensional (2-D) systems with asynchronous-delay under binary encoding-decoding schemes with probabilistic bit flips. The key contributions include: 1) a decoded measurement reconstruction method to effectively utilize delayed measurement information, 2) a new event-triggered mechanism definition considering the bidirectional signal propagation in 2-D systems, 3) a recursive estimator framework that accommodates estimation gain perturbations, and 4) a rigorous analysis of the monotonicity of filtering performance with respect to triggering parameters.

Abstract

The paper investigates the event-triggered resilient filtering problem for a class of 2-D systems with asynchronous-delay under binary encoding-decoding schemes with probabilistic bit flips. The key highlights are:

To reduce unnecessary communications and computations, a new event-triggered mechanism is proposed that focuses on broadcasting necessary measurement information to update innovation only when the event generator function is satisfied.

A binary encoding-decoding scheme is used to quantify the measurement information into a bit stream, transmit it through a memoryless binary symmetric channel with a certain probability of bit flipping, and restore it at the receiver.

To utilize the delayed decoded measurement information, a measurement reconstruction approach is proposed. It is shown that the reconstructed delay-free decoded measurement sequence contains the same information as the original delayed decoded measurement sequence.

A resilient filter is utilized to accommodate possible estimation gain perturbations. A recursive estimator framework is presented based on the reconstructed decoded measurement sequence.

The unbiased property of the proposed estimator is proved using mathematical induction. The estimation gain is obtained by minimizing an upper bound on the filtering error covariance.

The monotonicity of filtering performance with respect to triggering parameters is discussed through rigorous mathematical analysis.

Stats

The system model is described by the Fornasini-Marchesini (FM-II) state-space representation with nonlinear functions satisfying certain Lipschitz conditions.
The measurement model considers asynchronous-delays across multiple channels.
The event generator function and the internal dynamic variable governing the event-triggering mechanism are defined.
The binary encoding-decoding scheme is characterized, including the properties of the encoding truncation error and the decoded output through the memoryless binary symmetric channel.

Quotes

"To reduce unnecessary communications and computations in complex network systems, alleviate network energy consumption, and optimize the use of network resources, a new event-triggered mechanism is proposed, which focuses on broadcasting necessary measurement information to update innovation only when the event generator function is satisfied."
"A binary encoding-decoding scheme is used in the communication process to quantify the measurement information into a bit stream, transmit it through a memoryless binary symmetric channel with a certain probability of bit flipping, and restore it at the receiver."
"In order to utilize the delayed decoded measurement information that a measurement reconstruction approach is proposed. Through generating space equivalence verification, it is found that the reconstructed delay-free decoded measurement sequence contains the same information as the original delayed decoded measurement sequence."

Key Insights Distilled From

Event-Triggered Resilient Filtering for 2-D Systems with Asynchronous-Delay: Handling Binary Encoding Decoding with Probabilistic Bit Flips

by Yu Chen,Wei ... at arxiv.org 04-26-2024

https://arxiv.org/pdf/2404.16408.pdf

Deeper Inquiries

How can the proposed event-triggered resilient filtering approach be extended to handle more complex network topologies or multi-agent systems

The proposed event-triggered resilient filtering approach can be extended to handle more complex network topologies or multi-agent systems by incorporating additional communication constraints and system dynamics. For complex network topologies, the triggering conditions can be adjusted to account for different communication delays, packet losses, or network congestion. By considering the interconnections between multiple agents in a system, the event-triggered mechanism can be designed to trigger based on collective events or consensus among agents. This can help in reducing unnecessary communication and computation in a distributed system while ensuring that the filtering process is resilient to network disturbances.

What are the potential trade-offs between the communication overhead reduction and the estimation accuracy when adjusting the triggering parameters

There are potential trade-offs between communication overhead reduction and estimation accuracy when adjusting the triggering parameters in the event-triggered resilient filtering approach. By increasing the threshold for triggering events, the communication overhead can be reduced as fewer transmissions are required. However, this may lead to a delay in updating the estimation, potentially affecting the accuracy of the filtering process. On the other hand, decreasing the threshold for triggering events can improve estimation accuracy by updating more frequently but may result in higher communication overhead. Finding the right balance between these trade-offs is crucial in optimizing the performance of the filtering system.

Can the techniques developed in this work be applied to other types of dynamic systems beyond 2-D systems, such as time-varying or nonlinear systems

The techniques developed in this work for event-triggered resilient filtering can be applied to other types of dynamic systems beyond 2-D systems, such as time-varying or nonlinear systems. By adapting the event-triggered mechanism and filtering algorithms to accommodate the specific characteristics of different dynamic systems, the approach can be extended to handle a wide range of system models. For time-varying systems, the triggering conditions can be adjusted to account for changes in system dynamics over time. In the case of nonlinear systems, the filtering algorithms can be modified to handle the nonlinearity in the system dynamics and measurement equations. Overall, the principles of event-triggered resilient filtering can be generalized to various types of dynamic systems with appropriate modifications.