Method of signal processing in weigh-in-motion of vehicles
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摘要: 为了提高汽车动态称重(WIM, Weigh-In-Motion)系统的称重精度,基于"逆模型"的思想,使用了自适应逆滤波器来充分抑制混叠在重量信号频带内的噪声信号.在有限长冲激响应滤波器的框架下,通过最小均方自适应算法,离线构建了WIM系统的逆系统,并用此逆系统作为一个新型的滤波器使用.而且,为了进一步提高称重精度,同时又采用了低通滤波器,来滤除分布在重量信号频带以外的噪声干扰.经过由自适应逆滤波器和低通滤波器构成的复合滤波器处理后, 称重结果较经参数估计方法处理后得到的结果,在称重精度上有了很大提高.Abstract: Based on the concept of inverse model, an adaptive inverse filter (AIF) was employed which could suppress noise within the bandwidth of the desired signal. Within the framework of the finite impulse response (FIR) filter, the inverse system of weigh-in-motion (WIM) system was constructed by using least mean square (LMS) adaptive algorithm as an innovative filter. Moreover, for the sake of best improving measurement accuracy, as a noise filter, a low-pass (LP) filter dedicated to restrain noise out of the bandwidth of useful signal was adopted. After processed by cascaded filter combination, namely, AIF filter and LP filter, obtained results, compared with those processed by the approach of parameter estimation, show a significant improvement in estimation of static weight of moving vehicles.
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Key words:
- weigh-in-motion (WIM) /
- inverse filtering /
- adaptive algorithm /
- signal processing
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