Empirical Noise Profiling and Dimensionality Reduction of Raw Triaxial Acceleration Data in Consumer-Grade MEMS Sensors

Engineering notes

Key topics: autonomous driving, control. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Consumer grade Micro Electro Mechanical Systems (MEMS) accelerometers are ubiquitous in autonomous navigation platforms but remain susceptible to inherent stochastic noise and deterministic biases.This study presents a comprehensive empirical noise profiling and dimensionality reduction analysis of raw, high-frequency triaxial acceleration data acquired from an MPU-6050 sensor under controlled, static conditions.A continuous dataset comprising 37,459 samples was captured using a Raspberry Pi Pico microcontroller.The analytical framework employed descriptive statistics, dual method anomaly detection (Interquartile Range and Z-score), and Principal Component Analysis (PCA) to rigorously evaluate signal integrity and multivariate covariance structures.Statistical profiling revealed a significant factory bias offset along the X axis (approximately 1,432 LSB) and an elevated, independent noise floor along the Z-axis (standard deviation of 493.2 LSB), in contrast to the stable, gravity aligned Y-axis.Furthermore, anomaly detection highlighted transient data corruption events, likely stemming from I²C communication glitches or power fluctuations.Crucially, PCA demonstrated that 75.1% of the total dataset variance is captured by the first two components: PC1 captures shared systemic noise across the X and Y axes and magnitude error, while PC2 isolates the distinct, orthogonal dynamics of the Z-axis.These findings validate the efficacy of PCA-based dimensionality reduction and targeted, axis specific filtering.By projecting high dimensional sensor streams onto principal components, resource constrained embedded systems can achieve substantial computational savings while preserving essential signal characteristics, ultimately facilitating the development of resilient, self calibrating inertial navigation algorithms.

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