Event-Triggered State Estimation Through Confidence Level

Keskeiset käsitteet

Proposing a novel event-triggered scheme based on confidence level for state estimation in discrete-time linear systems.

Tiivistelmä

The paper introduces a novel event-triggered scheme based on confidence level for state estimation in discrete-time linear systems. It discusses the importance of reducing communication costs in wireless sensor networks and presents algorithms for estimating communication rates. The proposed Minimum Mean Squared Error (MMSE) state estimator is illustrated using a target tracking scenario.
Structure:

Introduction to Event-Triggered State Estimation
Proposed Event-Triggered Scheme Based on Confidence Level
MMSE State Estimation Algorithm
Communication Rate Estimation Algorithms
Simulation Example: Performance Evaluation

Tilastot

"Two algorithms for communication rate estimation of the proposed MMSE state estimator are developed."
"The simulation results show that the second algorithm yields a better communication rate estimation."
"For the tolerable upper bound N, we take three different parameter values given by three cases."

Lainaukset

Tärkeimmät oivallukset

Event-Triggered State Estimation Through Confidence Level

by Wei Liu klo arxiv.org 03-25-2024

https://arxiv.org/pdf/2403.15289.pdf

Syvällisempiä Kysymyksiä

How can the proposed event-triggered scheme improve performance compared to existing methods

The proposed event-triggered scheme improves performance compared to existing methods by establishing a proper connection between the tolerable upper bound of the innovation covariance, the innovation, and the trigger threshold through confidence level and chi-square distribution. This allows for a more precise determination of when communication is necessary based on the confidence level, leading to better decision-making regarding data transmission. By incorporating these elements into the triggering mechanism, the scheme can strike a balance between communication cost and estimation performance effectively.

What are the implications of using different tolerable upper bound values on the system's performance

Using different tolerable upper bound values has implications on system performance. The choice of N affects how often data transmission occurs based on whether Nk exceeds this limit or not. Higher values of N result in fewer transmissions as it requires larger innovations to trigger communication. This can lead to reduced communication costs but may also impact estimation accuracy if important information is not transmitted promptly. On the other hand, lower values of N increase communication frequency but may improve real-time tracking capabilities at the expense of higher energy consumption.

How can these algorithms be adapted for real-world applications beyond target tracking scenarios

These algorithms can be adapted for real-world applications beyond target tracking scenarios by applying them in various sensor network systems where remote estimation is required with limited communication resources. For example:

In environmental monitoring networks, such algorithms could optimize data transmission from sensors to central estimators based on confidence levels.
In industrial IoT systems, they could enhance predictive maintenance strategies by efficiently transmitting sensor data only when significant changes are detected.
In smart grid applications, they could improve state estimation processes while minimizing energy usage for wireless communications.
By customizing parameters like tolerable bounds and thresholds according to specific application requirements, these algorithms can be tailored for diverse use cases in practical settings outside traditional target tracking scenarios.