The Clustering Vector ARMA Temperature Sensor Of Railway Vehicle Axles Based On Nios

In developing the optical fiber temperature sensor and the optical fiber data transmitting system of railway vehicle axles based on Nios, one of the problems comes down to random signal processing. The Kalman Optimal Smoothing Filter based on state space model is transformed to the Kalman Optimal Smoothing Filter based on clustering vector ARMA according to the clustering vector ARMA (Autoregressive Moving Average) model, which is the principal idea and one of the key techniques in modern time sequence analytical method. In this paper the clustering arithmetic is expatiated, the design method for realizing the Kalman Optimal Smoothing Filter based on clustering vector ARMA 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.In the future, very few electronic equipment can exist without programmable logic. Programmability is the future of electronic system design. Fixed function SOC's (System-on-a-Chips) will become less and less practical. The high development cost, long development time and lack of flexibility often render SOC's to be obsolete soon after they come out of initial production. Altera pioneered SOPC by putting the "P" in "SOC". P=Programmability=Flexibility. Nios, Altera's popular soft core RISC CPU, is at the heart of SOPC design. Altera DSP Builder can designthe high speed subsystem for Nios. Its characteristic is module feature design.The Clustering Vector ARMA Temperature Sensor system of Railway Vehicle AxlesBased on Nios include FIFO module, C_Clustering module, the minimum variance...