We present , a localization system to estimate the location and the traveling distance by leveraging the lower-power inertial sensors embedded in smartphones as a supplementary to GPS. To minimize the negative impact of sensor noises, exploits the intermittent strong GPS signals and uses the linear regression to build a prediction model which is based on the trace estimated from inertial sensors and the one computed from the GPS. Furthermore, we utilize landmarks (e.g., bridge, traffic lights) detected automatically and special driving patterns (e.g., turning, uphill, and downhill) from inertial sensory data to improve the localization accuracy when the GPS signal is weak. Our evaluations of in the city demonstrates its technique viability and significant localization accuracy improvement compared with GPS and other approaches: the error is approximately 20m for 90 of time while the known mean error of GPS is 42.22m.
Recently, dead-reckoning strategies using internal sensors to estimate motion activities have attracted many research interests. Strapdown Inertial Navigation System (SINS) @cite_5 and Pedometer System @cite_25 use MEMS to estimate the moving speed and trace. The key issue is to deal with the noise of internal sensors and accumulated errors, which sometimes grow cubically @cite_31 . Personal Dead-reckoning (PDR) system @cite_15 uses Zero Velocity Update'' to calibrate the drift. The majority of the dead-reckoning studies focus on walking estimation, such as UnLoc @cite_28 , and CompAcc @cite_0 . Their main idea is to use accelerometer sensors to estimate the number of walking steps, and then measure the walking distance. AutoWitness @cite_22 is the system with an embedded wireless tag integrated with vibration, accelerometer, and gyroscope sensors. The tag is attached to a vehicle, and accelerometer and gyroscope sensors are used to track the moving trace.
@cite_22 @cite_15 @cite_28 @cite_0 @cite_5 @cite_31 @cite_25
2044831755 2137444221 2100801339 2161273285
With the continual miniaturisation of sensors and processing nodes, Pedestrian Dead Reckoning (PDR) systems are becoming feasible options for indoor tracking. These use inertial and other sensors, often combined with domain-specific knowledge about walking, to track user movements. There is currently a wealth of relevant literature spread across different research communities. In this survey, a taxonomy of modern PDRs is developed and used to contextualise the contributions from different areas. Techniques for step detection, characterisation, inertial navigation and step-and-heading-based dead-reckoning are reviewed and compared. Techniques that incorporate building maps through particle filters are analysed, along with hybrid systems that use absolute position fixes to correct dead-reckoning output. In addition, consideration is given to the possibility of using smartphones as PDR sensing devices. The survey concludes that PDR techniques alone can offer good short- to medium- term tracking under certain circumstances, but that regular absolute position fixes from partner systems will be needed to ensure long-term operation and to cope with unexpected behaviours. It concludes by identifying a detailed list of challenges for PDR researchers. This paper introduces a positioning system for walking persons, called "Personal Dead-reckoning" (PDR) system. The PDR system does not require GPS, beacons, or landmarks. The system is therefore useful in GPS-denied environments, such as inside buildings, tunnels, or dense forests. Potential users of the system are military and security personnel as well as emergency responders. The PDR system uses a small 6-DOF inertial measurement unit (IMU) attached to the user's boot. The IMU provides rate-of-rotation and acceleration measurements that are used in real-time to estimate the location of the user relative to a known starting point. In order to reduce the most significant errors of this IMU-based system−caused by the bias drift of the accelerometers−we implemented a technique known as "Zero Velocity Update" (ZUPT). With the ZUPT technique and related signal processing algorithms, typical errors of our system are about 2 of distance traveled. This typical PDR system error is largely independent of the gait or speed of the user. When walking continuously for several minutes, the error increases gradually beyond 2 . The PDR system works in both 2-dimensional (2-D) and 3-D environments, although errors in Z-direction are usually larger than 2 of distance traveled. Earlier versions of our system used an impractically large IMU. In the most recent version we implemented a much smaller IMU. This paper discussed specific problems of this small IMU, our measures for eliminating these problems, and our first experimental results with the small IMU under different conditions. This paper presents a method for correcting dead reckoning parameters, which are heading and step size, for a pedestrian navigation system. In this method, the compass bias error and the step size error can be estimated during the period that the Global Positioning System (GPS) signal is available. The errors are used for correcting those parameters to improve the accuracy of position determination using only the dead reckoning system when the GPS signal is not available. The results show that the parameters can be estimated with reasonable accuracy. Moreover, the method also helps to increase the positioning accuracy when the GPS signal is available. We studied the feasibility of estimating walking speed using a shank-mounted inertial measurement unit. Our approach took advantage of the inverted pendulum-like behavior of the stance leg during walking to identify a new method for dividing up walking into individual stride cycles and estimating the initial conditions for the direct integration of the accelerometer and gyroscope signals. To test its accuracy, we compared speed estimates to known values during walking overground and on a treadmill. The speed estimation method worked well across treadmill speeds and slopes yielding a root mean square speed estimation error of only 7 . It also worked well during overground walking with a 4 error in the estimated travel distance. This accuracy is comparable to that achieved from foot-mounted sensors, providing an alternative in sensor positioning for walking speed estimation. Shank mounted sensors may be of great benefit for estimating speed in walking with abnormal foot motion and for the embedded control of knee-mounted devices such as prostheses and energy harvesters.