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Event-triggered Kalman-consensus filter for two-target tracking sensor networks.

Housheng Su1, Zhenghao Li1, Yanyan Ye1

  • 1School of Automation, Image Processing and Intelligent Control Key Laboratory of Education Ministry of China, Huazhong University of Science and Technology, Luoyu Road 1037, Wuhan 430074, China.

ISA Transactions
|June 29, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces an event-triggered Kalman-consensus filter for tracking multiple targets in sensor networks. The proposed method enhances distributed filtering stability and effectiveness using neighbor information exchange.

Keywords:
Event-triggeredKalman-consensus filterSensor networkSuboptimalTwo-target

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Area of Science:

  • Control Systems Engineering
  • Networked Sensor Systems
  • Target Tracking

Background:

  • Distributed sensor networks require efficient data processing for target tracking.
  • Kalman filtering is a standard but often communication-intensive method for state estimation.
  • Event-triggered communication can reduce network load in distributed systems.

Purpose of the Study:

  • To develop an event-triggered Kalman-consensus filter for multi-target tracking in sensor networks.
  • To ensure system stability and efficient information sharing among neighboring sensors.
  • To reduce communication overhead while maintaining tracking accuracy.

Main Methods:

  • Derivation of a suboptimal Kalman gain matrix using mean-square analysis and an event-triggered protocol.
  • Design of a distributed filter where sensors rely on neighbor information.
  • Application of Lyapunov stability theory and matrix theory to guarantee system stability.

Main Results:

  • A suboptimal event-triggered distributed Kalman-consensus filter was successfully designed.
  • Sufficient conditions for system stability were established.
  • Simulations demonstrated the effectiveness of the proposed event-triggered protocol for two-target tracking.

Conclusions:

  • The developed event-triggered Kalman-consensus filter provides a stable and effective solution for distributed multi-target tracking.
  • The approach balances tracking performance with reduced communication requirements.
  • This method is suitable for resource-constrained sensor network applications.