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Stochastic error in the Micro-Electro-Mechanical-System (MEMS) Strapdown Inertial Navigation Unit (SINU) is the primary issue causing sensors to be unable to operate as a standalone device. Conventional implementation of MEMS SINU fuses measurement with a global positioning system (GPS) through a Kalman filter in order to achieve long-term accuracy. Such integration is known as a GPS-aided SINU system, and its estimation accuracy relies on how precise the stochastic error prediction is in Kalman filtering operation. In this paper, a comprehensive study on stochastic error modeling and analysis through a Gauss-Markov (GM) model and autoregressive (AR) model are presented. A wavelet denoising technique is introduced prior to error modeling to remove the MEMS SINU's high frequency noise. Without a wavelet denoising technique, neither the GM model nor AR model can be utilized to represent the …