Zhang Chuanhao, Li Changsheng, Chen Zhipeng, Li Haojie, Yu Hang
School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Sensors (Basel). 2025 Apr 14;25(8):2462. doi: 10.3390/s25082462.
To achieve precise collaborative localization of multiple unmanned aerial vehicles (UAVs) in hardware environments, this paper presents an field-programmable gate array-based particle swarm optimization (PSO) algorithm aimed at enhancing the localization efficiency of multiple nodes targeting a specific object. By leveraging the unique computational capabilities of FPGA, the proposed algorithm integrates optimization strategies, including particle mutation, variable crossover probabilities, and adjustable weights. These strategies collectively enhance the performance of the PSO algorithm in localization tasks. Comparative simulations conducted across a range of operational scenarios demonstrate that the algorithm not only ensures high localization accuracy but also delivers excellent real-time performance and rapid convergence. To further validate the algorithm's practical applicability, a four-node collaborative localization platform was developed, and experiments were carried out. The results confirmed the feasibility of multi-node collaborative localization, underscoring the advantages of the proposed algorithm, such as high accuracy, fast convergence, and robust stability.
为了在硬件环境中实现多架无人机（UAV）的精确协同定位，本文提出了一种基于现场可编程门阵列的粒子群优化（PSO）算法，旨在提高多个节点针对特定目标的定位效率。通过利用FPGA独特的计算能力，该算法集成了包括粒子变异、可变交叉概率和可调权重在内的优化策略。这些策略共同提高了PSO算法在定位任务中的性能。在一系列操作场景下进行的对比模拟表明，该算法不仅确保了高定位精度，还具有出色的实时性能和快速收敛性。为了进一步验证该算法的实际适用性，开发了一个四节点协同定位平台并进行了实验。结果证实了多节点协同定位的可行性，突出了所提算法的优势，如高精度、快速收敛和强大的稳定性。
