Decentralized cooperative TOA/AOA target tracking for hierarchical wireless sensor networks

Area of Science:

• Computer Science
• Electrical Engineering
• Robotics

Background:

• Hierarchical wireless sensor networks (WSNs) face challenges in distributed target tracking.
• Efficient object positioning requires robust localization algorithms within clustered network topologies.

Purpose of the Study:

• To propose a distributed, energy-efficient method for cooperative target tracking in hierarchical WSNs.
• To enhance object positioning accuracy through adaptive sub-cluster selection and leader-based information fusion.

Main Methods:

• A leader-based information processing approach within a cluster-based WSN topology.
• Adaptive sub-cluster selection using random timers and local network information.
• Local target position estimation via Bayesian filtering and neural networking models.
• Estimation fusion at the leader node using the covariance intersection algorithm.

Main Results:

• The proposed algorithm enables energy-efficient, distributed target tracking.
• Accurate object position estimation is achieved through local processing and centralized fusion.
• The protocol design demonstrates a balance between efficiency and practicality in WSNs.

Conclusions:

• The developed distributed method offers an effective solution for cooperative target tracking in hierarchical WSNs.
• The integration of Bayesian filtering, neural networks, and covariance intersection enhances localization performance.
• This research contributes to the development of more practical and efficient algorithms for object position estimation in WSNs.