KEYWORDS: Magnetism, Signal filtering, Detection and tracking algorithms, Received signal strength, Error analysis, Roads, Databases, Matrices, Gyroscopes, Tunable filters
Indoor positioning technology is one of the core technologies for location services and artificial intelligence applications and will play a very important role in people’s future lives. In order to solve the location problem in a complex indoor environment, this paper presents an indoor positioning method based on Pedestrian Dead Reckoning (PDR) and map matching. This method first uses PDR method to estimate pedestrian’s walking trajectory. Secondly, PDR estimation errors will be corrected by using the map matching algorithms. Finally, the Kalman filter will fuse the fixed PDR results with the Wi-Fi and geomagnetic fingerprints. Preliminary research shows that this method can provide confidence in the indoor positioning scenes, which can be used in malls, museums, supermarkets, airports, and other places.
In order to provide accurate ship-motion prediction for safe seaboard operations, this paper presents a new ship-motion prediction algorithm based on modified covariance (MCOV) method and neural networks. This algorithm firstly uses the MCOV method to analysis spectrums of the ship motion. And then, major spectrums of the ship motion are used to find the ship motion model by using the neural network (NN). Simulation results show that this method can present a confidence performs on ship-motion prediction.
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