This paper provides an overview of the most significant existing methods for indoor positioning on a contemporary smartphone. The approaches include Wi-Fi and Bluetooth based positioning, magnetic field fingerprinting, map aided navigation using building floor plans, and aiding from self-contained sensors. Wi-Fi and Bluetooth based positioning methods considered in this survey are fingerprint approaches that determine a user's position using a database of radio signal strength measurements that were collected earlier at known locations. Magnetic field fingerprinting can be used in an information fusion algorithm to improve positioning. The map-matching algorithms include application of wall constraints, topological indoor maps, and building geometry for heading correction. Finally, methods for step counting, step length and direction estimation, orientation tracking, motion classification, transit mode detection, and floor change detection in multi-storey buildings are discussed.

A Survey of Selected Indoor Positioning Methods for Smartphones

Most ultrawideband (UWB) location systems already proposed for position estimation have only been individually evaluated for particular scenarios. For a fair performance comparison among different solutions, a common evaluation scenario would be desirable. In this paper, we compare three commercially available UWB systems (Ubisense, BeSpoon, and DecaWave) under the same experimental conditions, in order to do a critical performance analysis. We include the characterization of the quality of the estimated tag-to-sensor distances in an indoor industrial environment. This testing space includes areas under line-of-sight (LOS) and diverse non-LOS conditions caused by the reflection, propagation, and the diffraction of the UWB radio signals across different obstacles. The study also includes the analysis of the estimated azimuth and elevation angles for the Ubisense system, which is the only one that incorporates this feature using an array antenna at each sensor. Finally, we analyze the 3-D positioning estimation performance of the three UWB systems using a Bayesian filter implemented with a particle filter and a measurement model that takes into account bad range measurements and outliers. A final conclusion is drawn about which system performs better under these industrial conditions.

Comparing Ubisense, BeSpoon, and DecaWave UWB Location Systems: Indoor Performance Analysis

Wireless indoor positioning has been extensively studied for the past 2 decades and continuously attracted growing research efforts in mobile computing context. As the integration of multiple inertial sensors (e.g., accelerometer, gyroscope, and magnetometer) to nowadays smartphones in recent years, human-centric mobility sensing is emerging and coming into vogue. Mobility information, as a new dimension in addition to wireless signals, can benefit localization in a number of ways, since location and mobility are by nature related in the physical world. In this article, we survey this new trend of mobility enhancing smartphone-based indoor localization. Specifically, we first study how to measure human mobility: what types of sensors we can use and what types of mobility information we can acquire. Next, we discuss how mobility assists localization with respect to enhancing location accuracy, decreasing deployment cost, and enriching location context. Moreover, considering the quality and cost of smartphone built-in sensors, handling measurement errors is essential and accordingly investigated. Combining existing work and our own working experiences, we emphasize the principles and conduct comparative study of the mainstream technologies. Finally, we conclude this survey by addressing future research directions and opportunities in this new and largely open area.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=5542&context=sis_research

Mobility Increases Localizability: A Survey on Wireless Indoor Localization using Inertial Sensors

Indoor location based services challenges, requirements and usability of current solutions

The Global Positioning System (GPS) can be readily used for outdoor localization, but GPS signals are degraded in indoor environments. How to develop a robust and accurate indoor localization system is an emergent task. In this paper, we propose a smartphone inertial sensor-based indoor localization and tracking system with occasional iBeacon corrections. Some important issues in a smartphone-based pedestrian dead reckoning (PDR) approach, i.e., step detection, walking direction estimation, and initial point estimation, are studied. One problem of the PDR approach is the drift with walking distance. We apply a recent technology, iBeacon, to occasionally calibrate the drift of the PDR approach. By analyzing iBeacon measurements, we define an efficient calibration range where an extended Kalman filter is utilized. The proposed localization and tracking system can be implemented in resource-limited smartphones. To evaluate the performance of the proposed approach, real experiments under two different environments have been conducted. The experimental results demonstrated the effectiveness of the proposed approach. We also tested the localization accuracy with respect to the number of iBeacons.

Smartphone Inertial Sensor-Based Indoor Localization and Tracking With iBeacon Corrections

The Extended Kalman Filter (EKF) has been the state of the art in Pedestrian Dead-Reckoning for foot-mounted Inertial Measurements Units. However due to the non-linearity in the propagation of the orientation the EKF is not the optimal Bayesian filter. We propose the usage of the Unscented Kalman Filter (UKF) as the integration algorithm for the inertial measurements. The UKF improves the mean and covariance propagation needed for the Kalman filter. Although the UKF provides a better estimate of the orientation, with Zero velocity UPdaTes (ZUPT) measurements, the yaw and the bias in the gyroscope associated with it becomes unobserved and might generate errors in the positioning. We studied the changes in the magnetic field during the stance phase and their relationship with the turn rates to propose three measurements using the magnetometer signal that will be called Magnetic Angular Rate Updates (MARUs). The first measurement uses the change in the angle of the magnetic field in the horizontal plane to measure the change in the yaw and provides a simple measurement for the UKF implementation. The second measurement relates the change in the magnetic field vector to the turn rate and provides information on the bias of the gyroscope for an UKF. The last measurement uses a first order approximation to generate a linear relationship with the gyroscope bias and therefore it can be used in an EKF. Finally we proposed a metric for the reliability of the stance as a way to use the pre and post stance information but adjusting the covariance of the measurements gradually from swing to stance. These methods were tested on real and simulated signals and they have shown improvements over the original PDR algorithms.

/pdf/unscented-kalman-filter-and-magnetic-angular-rate-update-2be6z9pxsf.pdf

Unscented Kalman filter and Magnetic Angular Rate Update (MARU) for an improved Pedestrian Dead-Reckoning

Unlike outdoor positioning, there is no unique solution to obtain the position of a person inside a building or in Global Navigation Satellite System (GNSS)-denied areas. Typical implementations indoor rely on dead reckoning or beacon-based positioning, but a robust estimation must combine several techniques to overcome their own drawbacks. In this paper, we present an indoor positioning system based on foot-mounted pedestrian dead reckoning (PDR) with an efficient map matching, received signal strength (RSS) measurements, and an improved motion model that includes the estimation of the turn rate bias. The system was implemented using a two-level structure with a low-level PDR filter and a high-level particle filter (PF) to include all the measurements. After studying the effect of the step displacement on the PFs proposed in the literature, we concluded that a new state with the turn rate bias (a nonobservable state in PDR) is needed to correctly estimate the error growth and, in the long term, correct the position and heading estimation. Additionally, the wall crossing detection of map matching was optimized as matrix operations, and a room grouping algorithm was proposed as a way to accelerate the process, achieving real-time execution with more than 100 000 particles in a building with more than 600 wall segments. We also include a basic path-loss model to use RSS measurements that allows a better initialization of the filter, fewer particles, and faster convergence, without the need for an extensive calibration. The inclusion of the map matching algorithm lowers the error level of the RSS-PDR positioning, from 1.9 to 0.75 m, 90% of the time. The system is tested in several trajectories to show the improvement in the estimated positioning, the time to convergence, and the required number of particles

Indoor Positioning Using Efficient Map Matching, RSS Measurements, and an Improved Motion Model

The main problem of Pedestrian Dead-Reckoning (PDR) using only a body-attached IMU is the accumulation of heading errors. The heading provided by magnetometers in indoor buildings is in general not reliable. Recently, a new method was proposed called Heuristic Drift Elimination (HDE) that minimizes the heading error when navigating in buildings. It assumes that the majority of buildings have their corridors parallel to each other, or they intersect at right angles, and consequently most of the time the person walks along a straight path with a heading constrained to one of four possible directions. In this paper we study the performance of HDE-based methods in complex buildings, i.e. with pathways also oriented at 45°, long curved corridors, and wide areas where non-oriented motion is possible. We explain how the performance of the original HDE method can be deteriorated in complex buildings. We also propose an improved HDE method called iHDE, that is implemented over a PDR framework that uses foot-mounted inertial navigation with an Extended Kalman Filter (EKF). The EKF is fed with the iHDE-estimated orientation error, as well as the confidence over that correction. We experimentally evaluated the performance of the proposed iHDE-based PDR method, comparing it with the original HDE implementation. Results show that both methods perform very well in ideal orthogonal narrow-corridor buildings, and iHDE outperforms HDE for non-ideal trajectories (e.g. curved paths).

/pdf/improved-heuristic-drift-elimination-ihde-for-pedestrian-56rn8utfyt.pdf

Improved Heuristic Drift Elimination (iHDE) for pedestrian navigation in complex buildings

The localization of persons in indoor environments is nowadays an open problem. There are partial solutions based on the deployment of a network of sensors (Local Positioning Systems or LPS). Other solutions only require the installation of an inertial sensor on the person’s body (Pedestrian Dead-Reckoning or PDR). PDR solutions integrate the signals coming from an Inertial Measurement Unit (IMU), which usually contains 3 accelerometers and 3 gyroscopes. The main problem of PDR is the accumulation of positioning errors due to the drift caused by the noise in the sensors. This paper presents a PDR solution that incorporates a drift correction method based on detecting the access ramps usually found in buildings. The ramp correction method is implemented over a PDR framework that uses an Inertial Navigation algorithm (INS) and an IMU attached to the person’s foot. Unlike other approaches that use external sensors to correct the drift error, we only use one IMU on the foot. To detect a ramp, the slope of the terrain on which the user is walking, and the change in height sensed when moving forward, are estimated from the IMU. After detection, the ramp is checked for association with one of the existing in a database. For each associated ramp, a position correction is fed into the Kalman Filter in order to refine the INS-PDR solution. Drift-free localization is achieved with positioning errors below 2 meters for 1,000-meter-long routes in a building with a few ramps.

https://www.mdpi.com/1424-8220/11/10/9393/pdf

PDR with a Foot-Mounted IMU and Ramp Detection

This paper presents a new indoor location concept named Light-matching which uses the perceived gradient in the illumination from unmodified indoor lights to achieve accurate physical location. The proposed method does not require any illumination calibration, just the pre-storage of the position and size of all lights in a building, irrespective of their current on/off state. The Light-matching method also requires the estimation of the relative displacement and orientation change of a person which is done by inertial Pedestrian Dead-Reckoning (PDR). Even from an initially unknown location and orientation, whenever the person passes below a switched-on light spot, the location likelihood is iteratively updated until it potentially converges to an unimodal probability density function. The time to converge to an unimodal position hypothesis depends on the number of lights detected and the asymmetries/irregularities of light distributions. The light-matching technique can be used alone or in cooperation with other signals of opportunity (WiFi, Magnetometers or Map-matching) to obtain a continuous high accuracy indoor localization system. This paper presents the basic description of the light-matching concept, the implementation details using a particle filter, and the evaluation of the method by simulation. The performance of the integrated solution can achieve a localization error lower than 1 m in most of the cases.

Francisco Zampella

Papers

Unscented Kalman filter and Magnetic Angular Rate Update (MARU) for an improved Pedestrian Dead-Reckoning

Indoor Positioning Using Efficient Map Matching, RSS Measurements, and an Improved Motion Model

Improved Heuristic Drift Elimination (iHDE) for pedestrian navigation in complex buildings

PDR with a Foot-Mounted IMU and Ramp Detection

Light-matching: A new signal of opportunity for pedestrian indoor navigation