Research on Enhancing Speed Measurement Accuracy in Rail Transit Systems Using Curve Fitting Techniques

Urban rail systems depend on speed sensors for velocity measurements, which often face challenges such as noise interference and sampling delay errors, particularly at low speeds. This study delves into the hardware structure, measurement principles, speed calculation methods, and the root causes and impacts of sampling errors on train control. A novel approach employing least squares curve fitting is proposed to tackle these issues. Our research endeavors to augment the efficacy and safety of urban rail systems by augmenting low-speed speed detection's precision and dependability. To authenticate the efficacy of the suggested technique, we conducted rigorous simulation tests. The study's results unveil auspicious strides in fortifying the security and steadfastness of rail operations, thereby accentuating the latent potential harbored within the proposed method to optimize urban rail systems.