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Smartphone-based Passive Entry & Passive Start (PEPS) has become a promising choice for next generation PEPS in automotive industry. Bluetooth Low Energy (BLE) with its low-cost, low power consumption, short delay and high reliability makes smartphone-based PEPS with the potential to replace the existing PEPS. Localization-based Service (LBS) in vehicles requires centimeter-level positioning accuracy and millisecond-Ievel response, which becomes a big challenge for Iocalization scheme and algorithm. This paper studies the critical problem of locating and identifying the interior and exterior ofvehicle based on BLE received signal strength (RSS). A comparative study on interior and exterior positioning in vehicle is presented in this paper by applying deterministic method, probabilistic method and pattern recognition method based on experiments in vehicle. Experiment results show that fingerprint-based probabilistic method gets higher Iocalization accuracy in vehicular application scenario than other methods.

In the AHRS using accelerometer can correct the error when calculating the attitude information of gyroscope. However, in the AHRS work process, the accelerometer measures the acceleration of gravity is affected by the external acceleration, and the accuracy is lost, which leads to the increase of the error of the attitude information. In order to estimate the external acceleration, this paper proposes to use Bootstrap to resample the three-axis accelerometer measurement to obtain regular data. According to the three-axis component of the accelerometer in the case of no external acceleration, the square sum is the principle of the gravitational acceleration value, the nonlinear least squares method is used to solve the external acceleration of each axis. Using MATLAB to read the numerical value based on the accelerometer to estimate the external acceleration data, and compare the standard values, analysis conclusions.