Microelectromechanical Systems (MEMS) technology is playing a key role in the design of the new generation of smartphones. Thanks to their reduced size, reduced power consumption, MEMS sensors can be embedded in above mobile devices for increasing their functionalities. However, MEMS cannot allow accurate autonomous location without external updates, e.g., from GPS signals, since their signals are degraded by various errors. When these sensors are fixed on the user's foot, the stance phases of the foot can easily be determined and periodic Zero velocity UPdaTes (ZUPTs) are performed to bound the position error. When the sensor is in the hand, the situation becomes much more complex. First of all, the hand motion can be decoupled from the general motion of the user. Second, the characteristics of the inertial signals can differ depending on the carrying modes. Therefore, algorithms for characterizing the gait cycle of a pedestrian using a handheld device have been developed. A classifier able to detect motion modes typical for mobile phone users has been designed and implemented. According to the detected motion mode, adaptive step detection algorithms are applied. Success of the step detection process is found to be higher than 97% in all motion modes.
In this paper a novel step length model using a handheld Micro Electrical Mechanical System (MEMS) is presented. It combines the user's step frequency and height with a set of three parameters for estimating step length. The model has been developed and trained using 12 different subjects: six men and six women. For reliable estimation of the step frequency with a handheld device, the frequency content of the handheld sensor's signal is extracted by applying the Short Time Fourier Transform (STFT) independently from the step detection process. The relationship between step and hand frequencies is analyzed for different hand's motions and sensor carrying modes. For this purpose, the frequency content of synchronized signals collected with two sensors placed in the hand and on the foot of a pedestrian has been extracted. Performance of the proposed step length model is assessed with several field tests involving 10 test subjects different from the above 12. The percentages of error over the travelled distance using universal parameters and a set of parameters calibrated for each subject are compared. The fitted solutions show an error between 2.5 and 5% of the travelled distance, which is comparable with that achieved by models proposed in the literature for body fixed sensors only.
This article presents a new tracking technique for sine-BOC(n, n) (or Manchester encoded) ranging signals, which will most likely be part of the new European Global Navigation Satellite System (GNSS), Galileo, signal plan. When traditional sine-BOC(n, n) tracking is considered, although offering excellent performance compared with current signals, it has the main drawback of potentially giving biased measurements. The new method presented herein allows the removal of this threat while maintaining the same level of performance. An adapted version of this technique can also be used for acquisition purposes.
Integration of GPS with inertial sensors can provide many benefits for navigation, from improved accuracy to increased reliability. The extent of such benefits, however, is typically a function of the quality of the inertial system used. Traditionally, high-cost, navigation-grade inertial measurement units (IMUs) have been used to obtain the highest position and velocity accuracies. However, the work documented in this paper uses a Honeywell HG-1700 IMU (1 deg/h) to assess the benefits of a tactical-grade IMU in aiding GPS for high-accuracy (centimeter-level) applications. To this end, the position and velocity accuracy of the integrated system during complete and partial GPS data outages is investigated. The benefit of using inertial data to improve the ambiguity resolution process after such data outages is also addressed in detail. Centralized and decentralized filtering strategies are compared in terms of system performance.
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