基于双闭环与自适应卡尔曼滤波的目标追踪系统

Target Tracking System Based on Dual Closed-loop and Adaptive Kalman Filtering

面对动态环境下机器人控制的挑战,现有的单闭环控制系统及传统滤波方法常因响应速度和稳定性的矛盾而难以达到理想的操作精度。为解决这一问题,研究提出了一种结合双闭环控制和自适应卡尔曼滤波的控制策略,以提高机器人的追踪和操控精度。通过自适应卡尔曼滤波优化视觉传感器和陀螺仪的数据处理,系统有效降低了传感器噪声并优化了数据质量。内环控制依据目标检测结果进行反馈调节,外环控制实现精准目标追踪。通过机器人平台移动控制系统对所提出的方法进行评价,试验结果表明,与单闭环系统和传统滤波方法相比,该策略在处理动态移动目标时展现出更高的精度和更优的稳定性,展示了其在复杂机器人操作场景中的实际应用价值。

Facing the challenges of robot control in dynamic environments,existing single closed-loop control systems and traditional filtering methods often struggle to achieve ideal operational precision due to the trade-off between response speed and stability.To address this issue,this study proposes a control strategy that combines dual closed-loop control and adaptive Kalman filtering to enhance the tracking and maneuvering precision of robots.By optimizing the data processing of visual sensors and gyroscopes through adaptive Kalman filtering,the system effectively reduces sensor noise and improves data quality.The inner loop control adjusts feedback based on target detection results,while the outer loop control achieves precise target tracking.The proposed method was evaluated using a robotic platform movement control system.The experimental results demonstrate that compared to single closed-loop systems and traditional filtering methods,this strategy shows higher accuracy and better stability in handling dynamic moving targets,showcasing its practical application value in complex robotic operation scenarios.