Distributed Target Tracking in Challenging Environments Using Multiple Asynchronous Bearing-Only Sensors.

Area of Science:

• Sensor networks
• Signal processing
• Data fusion

Background:

• Multiple asynchronous bearing-only (BO) sensors face challenges with clutter, target misdetection, and out-of-sequence (OOS) data.
• Existing systems struggle with kinematic state unobservability in single BO sensor scenarios.

Purpose of the Study:

• To propose a novel distributed tracking architecture addressing clutter and OOS data simultaneously.
• To enhance target measurement accuracy and data fusion in asynchronous sensor systems.

Main Methods:

• Developed a local integrated probabilistic data association (LIPDA) method for local tracking to mitigate clutter.
• Proposed a distributed IPDA-forward prediction fusion and decorrelation (DIPDA-FPFD) approach for central fusion of OOS information.
• Implemented track management at both local and central levels using probability of target existence.

Main Results:

• The LIPDA method effectively eliminates clutter disturbance and improves target measurement accuracy.
• The DIPDA-FPFD approach enables sequential fusion of OOS information from multiple sensors.
• The proposed architecture demonstrates efficient track management and data fusion capabilities.

Conclusions:

• The novel distributed tracking architecture successfully addresses key challenges in asynchronous BO sensor systems.
• The proposed LIPDA and DIPDA-FPFD methods offer significant improvements in target tracking accuracy and data fusion.
• Numerical experiments validate the efficiency and effectiveness of the developed methodology.