Joint Sensor Selection and Power Allocation Algorithm for Multiple-Target Tracking of Unmanned Cluster based on Fuzzy...

Area of Science:

• Aerospace Engineering
• Electrical Engineering
• Computer Science

Background:

• Unmanned aerial vehicle (UAV) clusters offer advantages over traditional manned platforms.
• Limited transmitting power on UAVs necessitates reduced power consumption for radar systems.
• Accurate multiple-target tracking (MTT) is crucial for effective UAV operations.

Purpose of the Study:

• To develop a joint radar node selection and power allocation algorithm for UAV radar networks.
• To reduce transmitting power while maintaining target detection accuracy.
• To enhance the suitability of radar systems for drone platforms.

Main Methods:

• Fuzzy logic reasoning (FLR) for target-to-radar priority assignment.
• Radar clustering algorithm to form sub-radar networks, simplifying MTT.
• Chance-constrained programming (CCP) to manage uncertainty in target radar cross-section (RCS) and balance power with tracking accuracy.

Main Results:

• The proposed algorithm effectively reduces power resource requirements for radar networks.
• A given tracking performance is achieved with significantly less power.
• The algorithm simplifies complex joint optimization problems by decomposing them into smaller sub-problems.

Conclusions:

• The developed algorithm enables efficient power management for UAV radar systems.
• The approach is well-suited for power-constrained drone platforms.
• Simulations confirm the algorithm's effectiveness in improving power efficiency for MTT.