AI 中文总结
本文设计实现了基于卡尔曼滤波的MPU6050倾角估计系统，在RP2040平台验证其可融合加速度计与陀螺仪数据，有效抑制噪声和漂移，性能优于单一传感器。
AI 中文摘要
准确的倾角估计在机器人、运动跟踪和嵌入式控制系统等众多工程应用中至关重要。然而，低成本惯性传感器的测量结果常受噪声和漂移影响。本文提出一种基于MPU6050惯性测量单元的单轴倾角估计系统，该系统在RP2040微控制器平台上实现，通过卡尔曼滤波完成传感器融合。加速度计可从重力直接估计倾角，但对噪声和短期波动敏感；陀螺仪能提供平滑的角速率测量，但长时间积分会引入漂移。为克服这些局限，采用卡尔曼滤波融合两种传感器的测量值，利用加速度计的长期稳定性和陀螺仪的短期平滑性。本文开展了仿真与硬件实验：仿真中对传感器噪声和漂移建模，以在受控条件下评估滤波器性能；硬件实现中，RP2040平台实时采集并处理MPU6050数据，将估计的倾角与仅加速度计、仅陀螺仪的输出对比。结果表明，所提方法可有效降低测量噪声、抑制长期漂移，同时保持良好的动态响应。总体而言，该系统的倾角估计比单一传感器更稳定准确，为嵌入式应用中基于卡尔曼滤波的传感器融合提供了实用且易实现的方案。
英文摘要
Accurate tilt angle estimation is important in many engineering applications, such as robotics, motion tracking, and embedded control systems. However, measurements from low-cost inertial sensors are often degraded by noise and drift. This paper presents a single-axis tilt angle estimation system based on the MPU6050 inertial measurement unit, implemented on an RP2040 microcontroller platform, with sensor fusion achieved through a Kalman filter. The accelerometer provides a direct estimate of tilt angle from gravity but is sensitive to noise and short-term fluctuations. The gyroscope provides smooth angular rate measurements, but integration over time introduces drift. To overcome these limitations, a Kalman filter is used to combine measurements from both sensors, leveraging the long-term stability of the accelerometer and the short-term smoothness of the gyroscope. Both simulation and hardware experiments are performed. In simulation, sensor noise and drift are modeled to evaluate the filter performance under control conditions. In the hardware implementation, real-time MPU6050 data is acquired and processed by the RP2040 platform, and the estimated tilt angle is compared with accelerometer-only and gyroscope-only outputs. The results show that the proposed method effectively reduces noise measurements and suppresses long-term drift while preserving good dynamic response. Overall, the system provides more stable and accurate tilt estimation than either sensor alone, demonstrating a practical and accessible approach for Kalman filter based sensor fusion in embedded application. This manuscript is a preprint version of the work. Keywords: Kalman Filter, Accelerometer, Gyroscope, Noise Reduction, Angle Tracking
Comments12 pages, 24 figures, 10 references
