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
2096416669 2137444221 2054602086 2099136733 2100801339
We present AutoWitness, a system to deter, detect, and track personal property theft, improve historically dismal stolen property recovery rates, and disrupt stolen property distribution networks. A property owner embeds a small tag inside the asset to be protected, where the tag lies dormant until it detects vehicular movement. Once moved, the tag uses inertial sensor-based dead reckoning to estimate position changes, but to reduce integration errors, the relative position is reset whenever the sensors indicate the vehicle has stopped. The sequence of movements, stops, and turns are logged in compact form and eventually transferred to a server using a cellular modem after both sufficient time has passed (to avoid detection) and RF power is detectable (hinting cellular access may be available). Eventually, the trajectory data are sent to a server which attempts to match a path to the observations. The algorithm uses a Hidden Markov Model of city streets and Viterbi decoding to estimate the most likely path. The proposed design leverages low-power radios and inertial sensors, is immune to intransit cloaking, and supports post hoc path reconstruction. Our prototype demonstrates technical viability of the design; the volume market forces driving machine-to-machine communications will soon make the design economically viable. 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. We propose UnLoc, an unsupervised indoor localization scheme that bypasses the need for war-driving. Our key observation is that certain locations in an indoor environment present identifiable signatures on one or more sensing dimensions. An elevator, for instance, imposes a distinct pattern on a smartphone's accelerometer; a corridor-corner may overhear a unique set of WiFi access points; a specific spot may experience an unusual magnetic fluctuation. We hypothesize that these kind of signatures naturally exist in the environment, and can be envisioned as internal landmarks of a building. Mobile devices that "sense" these landmarks can recalibrate their locations, while dead-reckoning schemes can track them between landmarks. Results from 3 different indoor settings, including a shopping mall, demonstrate median location errors of 1:69m. War-driving is not necessary, neither are floorplans the system simultaneously computes the locations of users and landmarks, in a manner that they converge reasonably quickly. We believe this is an unconventional approach to indoor localization, holding promise for real-world deployment. This paper identifies the possibility of using electronic compasses and accelerometers in mobile phones, as a simple and scalable method of localization without war-driving. The idea is not fundamentally different from ship or air navigation systems, known for centuries. Nonetheless, directly applying the idea to human-scale environments is non-trivial. Noisy phone sensors and complicated human movements present practical research challenges. We cope with these challenges by recording a person's walking patterns, and matching it against possible path signatures generated from a local electronic map. Electronic maps enable greater coverage, while eliminating the reliance on WiFi infrastructure and expensive war-driving. Measurements on Nokia phones and evaluation with real users confirm the anticipated benefits. Results show a location accuracy of less than 11m in regions where today's localization services are unsatisfactory or unavailable. 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.