The measurement error of the barometer equipped in smartphones is commonly modeled as a first-order Gaussian-Markov (GM) model, with the time constant and noise variance being two important parameters for establishing the model. In this paper, the time constant and variance of the barometer sensor in smartphones were determined by the Discrete Wavelet Transform (DWT) and the autocorrelation function, and the time constant is applied to the barometer calibration to verify the rationality of the parameters. The barometric altimeter calibration utilizes GPS altitude information as an aid, and the fusion algorithm adopts Kalman filtering. A set of different time constants was established to validate the time constant obtained through autocorrelation analysis. The experimental results indicate that this parameter is reasonable and reliable, providing an experimental basis and research foundation for the parameter selection of the first-order GM model for smartphone barometric pressure sensors.