FPGA-Based Particle Swarm Collaborative Target Localization Algorithm for UAV Swarms.

Area of Science:

• Robotics and Control Systems
• Computational Intelligence
• Aerospace Engineering

Background:

• Precise collaborative localization is critical for multi-unmanned aerial vehicle (UAV) systems.
• Existing methods may face challenges in real-time performance and accuracy in complex environments.

Purpose of the Study:

• To develop and validate a Field-Programmable Gate Array (FPGA)-based Particle Swarm Optimization (PSO) algorithm for enhanced multi-UAV collaborative localization.
• To improve localization efficiency, accuracy, and convergence speed using hardware acceleration and optimized PSO strategies.

Main Methods:

• Implementation of a PSO algorithm on an FPGA for parallel processing and hardware acceleration.
• Integration of optimization strategies: particle mutation, variable crossover probabilities, and adjustable weights.
• Simulation across diverse scenarios and experimental validation using a four-node collaborative localization platform.

Main Results:

• The FPGA-based PSO algorithm demonstrated high localization accuracy and excellent real-time performance.
• The system achieved rapid convergence in comparative simulations.
• Experimental results confirmed the feasibility and advantages of multi-node collaborative localization using the proposed algorithm.

Conclusions:

• The proposed FPGA-based PSO algorithm effectively enhances multi-UAV collaborative localization.
• The algorithm offers significant advantages in accuracy, convergence speed, and stability for practical applications.
• Hardware acceleration via FPGA is a viable approach for optimizing complex localization tasks in multi-UAV systems.