PURPOSES: This study is to predict the Sound Pressure Level(SPL) obtained from the Noble Close ProXimity(NCPX) method by using the Extended Kalman Filter Algorithm employing the taylor series and Linear Regression Analysis based on the least square method. The objective of utilizing EKF Algorithm is to consider stochastically the effect of error because the Regression analysis is not the method for the statical approach. METHODS: For measuring the friction noise between the surface and vehicle’s tire, NCPX method was used. With NCPX method, SPL can be obtained using the frequency analysis such as Discrete Fourier Transform(DFT), Fast Fourier Transform(FFT) and Constant Percentage Bandwidth(CPB) Analysis. In this research, CPB analysis was only conducted for deriving A-weighted SPL from the sound power level in terms of frequencies. EKF Algorithm and Regression analysis were performed for estimating the SPL regarding the vehicle velocities. RESULTS : The study has shown that the results related to the coefficient of determination and RMSE from EKF Algorithm have been improved by comparing to Regression analysis. CONCLUSIONS : The more the vehicle is fast, the more the SPL must be high. But in the results of EKF Algorithm, SPLs are irregular. The reason of that is the EKF algorithm can be reflected by the error covariance from the measurements.

PURPOSES : The objective of this study is to provide for the overall SPL (Sound Pressure Level) prediction model by using the NCPX (Noble Close Proximity) measurement method in terms of regression equations. METHODS: Many methods can be used to measure the traffic noise. However, NCPX measurement can powerfully measure the friction noise originated somewhere between tire and pavement by attaching the microphone at the proximity location of tire. The overall SPL(Sound Pressure Level) calculated by NCPX method depends on the vehicle speed, and the basic equation form of the prediction model for overall SPL was used, according to the previous studies (Bloemhof, 1986; Cho and Mun, 2008a; Cho and Mun, 2008b; Cho and Mun, 2008c). RESULTS : After developing the prediction model, the prediction model was verified by the correlation analysis and RMSE (Root Mean Squared Error). Furthermore, the correlation was resulted in good agreement. CONCLUSIONS: If the polynomial overall SPL prediction model can be used, the special cautions are required in terms of considering the interpolation points between vehicle speeds as well as overall SPLs.