Retransmission times dynamic adjustment method based on real-time packet loss information triggering

Abstract

The invention provides a retransmission times dynamic adjustment method based on real-time packet loss information triggering. The method comprises the steps of collecting a system state of a discretelinear time invariant system, carrying out Kalman filtering on the system state and then transmitting the system state to a remote estimator; on the basis of the feature that a remote state estimation error covariance is converged or sharply enlarged according to whether a data packet arrives or is lost, designing a retransmission demand degree index, and automatically selecting a change functionof the index, thereby adjusting increase and decrease amplitude of the index; designing retransmission times increase and decrease triggering thresholds for the discontinuously changing demand degreeindex; and in a transmission process, when the demand degree index reaches the increase and decrease triggering thresholds, increasing and decreasing the retransmission times, and updating the transmission times for next-time data transmission. According to the algorithm provided by the invention, under the same energy consumption, compared with a retransmission times equal distribution algorithm, the retransmission times dynamic adjustment algorithm has the advantage that the remote estimation performance can be optimized well.

Description

technical field [0001] The invention relates to the field of information physical systems in communication engineering, in particular to a method for dynamically adjusting the number of retransmissions triggered by real-time packet loss information. Background technique [0002] In cyber-physical systems, improving the performance of remote state estimation has been a concern of researchers, because in real life, remote state estimation plays an important role. In test applications, remote estimation can detect fires, earthquakes, and environmental pollution; industrial production requires real-time remote estimation to adjust control commands, and also relies on remote estimation for fault diagnosis and information security testing. [0003] So far, performance improvement methods for remote state estimation have achieved a lot of research results. However, there are still the following problems: 1) The channel state is uncertain, which may cause delays, bit errors or even...

AI Technical Summary

Problems solved by technology

However, there are still the following problems: 1) The channel state is uncertain, which may cause delays, bit errors or even loss of data packets during transmission, reducing the accuracy of remote estimation; 2) How to balance estimation performance and energy consumption The relationship between is also a problem that needs to be solved urgently; 3) The information transmission of large-scale control systems may face the threat of information security and new attacks

Images