Abstract
This paper proposes a pedestrian dead reckoning (PDR) algorithm based on the strap-down inertial navigation system (SINS) using the gyros, accelerometers, and magnetometers on smartphones. In addition to using a gravity vector, magnetic field vector, and quasi-static attitude, this algorithm employs a gait model and motion constraint to provide pseudo-measurements (i.e., three-dimensional velocity and two-dimensional position increment) instead of using only pseudo-velocity measurement for a more robust PDR algorithm. Several walking tests show that the advanced algorithm can maintain good position estimation compare to the existing SINS-based PDR method in the four basic smartphone positions, i.e., handheld, calling near the ear, swaying in the hand, and in a pants pocket. In addition, we analyze the navigation performance difference between the advanced algorithm and the existing gait-model-based PDR algorithm from three aspects, i.e., heading estimation, position estimation, and step detection failure, in the four basic phone positions. Test results show that the proposed algorithm achieves better position estimation when a pedestrian holds a smartphone in a swaying hand and step detection is unsuccessful.
Highlights
The rapid development of location-based services (LBSs) has resulted in great convenience in people’s daily lives: personal localization and navigation, and personal tracking and monitoring [1].A precise location is necessary to provide a high-quality LBS, and the global navigation satellite system (GNSS) can provide accurate locations for pedestrians in the outside open-sky environment.the GNSS faces the problems of signal blockage and multipath in urban areas and other challenging environments
A pedestrian’s walking heading is the main factor influencing the accuracy of position estimation after training a gait model for the pedestrian dead reckoning (PDR) algorithms
E-PDR always exhibits better navigation performance than normal PDR owing to its excellent heading accuracy, which is achieved by fusing the data from tri-gyroscopes, tri-accelerometers, and tri-magnetometers
Summary
The rapid development of location-based services (LBSs) has resulted in great convenience in people’s daily lives: personal localization and navigation, and personal tracking and monitoring [1].A precise location is necessary to provide a high-quality LBS, and the global navigation satellite system (GNSS) can provide accurate locations for pedestrians in the outside open-sky environment.the GNSS faces the problems of signal blockage and multipath in urban areas and other challenging environments (e.g., indoor environments). Indoor localization technology is flourishing, and many different techniques have been designed and developed for tracking pedestrians’ positions when in indoor environments, such as Wi-Fi [2,3], Bluetooth/iBeacon [4,5], radio frequency identification (RFID) [6,7], near-field communication (NFC) [8], ultra-wideband (UWB) [9], magnetic matching [10,11], and inertial-sensor-based [12]. Owing to the many kinds of application requirements and the large cost difference, all of the abovementioned indoor localization methods are at different levels of development. Wi-Fi signals, have poor stability in complex indoor environments, and can be blocked by the human body, as is the case with all other radio-frequency-signal-based indoor location methods. Mobile devices with a built-in micro-electro-mechanical system (MEMS)
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