The Performance Analysis of Smartphone-Based Pedestrian Dead Reckoning and Wireless Locating Technology for Indoor Navigation Application
Recent developments in smartphone technology have increased user demand for indoors applications. The Global Navigation Satellite System (GNSS) and the Inertial Navigation System (INS) are the two advanced systems for navigation technology. However, it is still difficult for GNSS to provide an accurate and practical navigation solution, especially in environments with little or no signal availability. These failings should be easy to overcome; because of their portability and multiple embedded
... ardware sensors, smartphones seem well positioned to make pedestrian navigation easy and convenient in any environment. Pedestrian Dead Reckoning (PDR) is one of the most commonly used technologies used for pedestrian navigation, but it is not always accurate because its positioning errors tend to accumulate over time. Therefore, this research introduces a new tool to overcome this failing; a Bluetooth Low-Energy (BLE) beacon can maintain and improve the accuracy of PDR. Moreover, a BLE beacon can be initialized from any user position in an indoor environment. The random and unpredictable positions of pedestrians inevitably result in the degradation of navigation guidance systems' data. To rectify this problem, we have implemented activity recognition technology to notify the proposed system so as to provide a more accurate heading estimate. This study proposes a Personal Navigation System (PNS) based on this technology; it can estimate navigation solutions in real time and combines the advantages of PDR and Bluetooth positioning technology. A series of experiments were conducted to evaluate the accuracy of the system and the efficacy of our proposed algorithms. Preliminary results show the average relative precision of PDR to be about 2.5%, when using a mobile hand-held device. The error of initial position from 2-D beacon positioning is less than two meters. The proposed system works well without post-processing, and the multi-sensor activity recognition system can determine the placement of the device when it is being carried or used by someone with close to 100% accuracy. Inventions 2016, 1, 25 2 of 19 propose a smartphone indoor positioning engine that only uses the built-in sensor and computational resources  . Though the accuracy of such MEMS-grade sensors leaves much to be desired, thanks to constant technological innovation, no one doubts that smaller size and better performance could be expected in the near future. The Pedestrian Dead Reckoning (PDR) has been extensively studied as an effective approach for estimating two-dimensional position for pedestrian navigation. It uses inertial sensors to detect human movement patterns, noting each step and estimating heading direction. However, unavoidable errors crept in due to the algorithm itself as well as the hardware sensors. MEMS sensors are often not very accurate, humans frequently alter speed, posture, and direction, and there is no fixed relationship between the sensors in the device and the human body. In addition, possible errors in step-count caused by an inadequately tuned threshold are hard to avoid due to inconsistencies in the behavior of the user. For example, when the threshold peak detection is used along with time interval, lower energy and duration may fail to detect the user's steps  , which lead to an accumulated position error. This problem is compounded because empirical formulas for estimating stride generally fail to accurately estimate users' step-length because of various individual characteristics and walking habits. In an attempt to overcome this problem, Ho et al. have proposed an adaptive step-length estimator based on a fast Fourier transform smoother and a set of rules to estimate step length accurately  . In addition, some advance methods of detecting steps and estimating their length accurately, such as waist-attached accelerometers  and knee-attached gyroscope  . Although these devices can measure movement much more accurately than other methods, to function correctly, the sensors need to be mounted on specific parts of the body, which general users will not be willing to do. In addition to the above errors, there is still another error source: heading (azimuth). Magnetometers and gyros are able to provide magnetic heading and angular rate, respectively, and the derived heading from a magnetometer is based upon the measurement of the Earth's magnetic field, so no errors will accumulate with time. However, the measured magnetic field will be significantly affected by the ambient indoor environment, including magnetic materials and obstacles that interrupt smooth readings and can even block them completely  . Gyroscopes, on the other hand, are relatively environment-independent sensors. Their measurement will not be affected by environmental conditions the way magnetometers can be. However, the nature of gyros causes them to require an initial heading, and their errors accumulate with time. As stated in the previous paragraph, PDR errors accumulate step-by-step because of inaccurate step count, step length and heading estimates. In order to maintain navigational accuracy, a PDR integrated with another external positioning system is needed. Indoor RF-based localization can be considered as the external positioning system for PDR because Bluetooth and Wi-Fi chips are generally embedded in smartphones. The RF-based localization systems commonly used for indoor positioning include Wi-Fi, RFID and Bluetooth. In most studies the received signal strength index (RSSI) has been used as the main measurement to navigate an indoor environment. Mohammadi and Boonsriwai et al. all used signal strength to locate a pedestrian in indoor environments [8, 9] . Because of the wide signal area, Wi-Fi based indoor positioning system are also employed. Fingerprinting, the pattern matching algorithm commonly used for Wi-Fi positioning, uses signal power strength to estimate position. The deployment of the fingerprinting method involves both a training and positioning phase. In the training step, signal power strength data (the location fingerprint) are collected and saved to a database. In the positioning step, the user's signal power strength is compared to the values stored in the database, after which the algorithm compares the real-time measurement to the fingerprints obtained in training and determines a position that best fits the fingerprints. Although a Wi-Fi installation has a broad coverage, the location of the Wi-Fi service is limited by the availability of a power supply. Other disadvantages are volume, a lack of privacy, and the distribution geometry. On the other hand, the newest version is Bluetooth 4.0, which includes Bluetooth Low-Energy (BLE) technology. Traditional Bluetooth has a significantly long scan time, which limits its value for localization. However, BLE has overcome the limitations of a long scan time and is supported by most current smartphones.