基于Nios的聚类向量ARMA轴温探测器——列车轴温探测系统的信号处理

阐述了在向量ARMA(AutoRegressive Moving Average，自回归滑动平均)模型中的聚类算法，介绍了使用Altera DSP Builder和SOPC Builder实现聚类向量自回归滑动平均的卡尔曼最优平滑滤波器的设计方法，给出了聚类向量ARMA卡尔曼最优平滑滤波器的设计框图，比较了32阶FIR（Finite Impulse Response ，有限冲击响应）滤波器与聚类向量ARMA卡尔曼最优平滑滤波器的滤波效果，同时还利用16阶DITFFT（时间抽取快速Fourier变换）展

Clustering Vector ARMA Temperature Sensor of Railway Vehicle Axles Based on Nios:Signal Processing for Temperature Sensor of Railway Vehicle Axles System

The clustering arithmetic is expatiated,the design method for realizing the Kalman Optimal Smoothing Filter based on clustering vector ARMA(Auto-Regressive Moving Average) by utilizing Altera DSP builder and SOPC builder is introduced,the designing frame figure concerning to the Kalman Optimal Smoothing Filter based on clustering vector ARMA is presented,and we compared 32 step FIR(Finite Impulse Response)with the Kalman Optimal Smoothing Filter based on clustering vector ARMA on the aspect of filter capacity.Meanwhile,the signals that buried deeply in the noises detected out by the 16 points DIT-FFT.It is Altera Nios embedded design base that realizes all the above functions.