The availability of location information has become a key factor in today's communications systems allowing location based services. In outdoor scenarios, the mobile terminal position is obtained with high accuracy thanks to the global positioning system (GPS) or to the standalone cellular systems. However, the main problem of GPS and cellular systems resides in the indoor environment and in scenarios with deep shadowing effects where the satellite or cellular signals are broken. In this paper, we survey different technologies and methodologies for indoor and outdoor localization with an emphasis on indoor methodologies and concepts. Additionally, we discuss in this review different localization-based applications, where the location information is critical to estimate. Finally, a comprehensive discussion of the challenges in terms of accuracy, cost, complexity, security, scalability, etc. is given. The aim of this survey is to provide a comprehensive overview of existing efforts as well as auspicious and anticipated dimensions for future work in indoor localization techniques and applications.

Recent Advances in Indoor Localization: A Survey on Theoretical Approaches and Applications

Numerical Methods for Scientists and Engineers

.............................................................................................................................................................. 6 1 Introduction ............................................................................................................................................. 7 1.1 Motivation .......................................................................................................................................................... 7 1.2 Previous Surveys ............................................................................................................................................. 8 1.3 Overview of Technologies ........................................................................................................................... 9 1.4 Indoor Positioning Applications ............................................................................................................ 11 1.5 Structure of this Work ............................................................................................................................... 14 2 User Requirements ............................................................................................................................. 15 2.1 Requirements Parameters Overview .................................................................................................. 15 2.2 Positioning Requirements Parameters Definition ......................................................................... 17 2.3 Man Machine Interface Requirements ................................................................................................ 19 2.4 Security and Privacy Requirements ..................................................................................................... 20 2.5 Costs .................................................................................................................................................................. 20 2.6 Generic Derivation of User Requirements......................................................................................... 20 2.7 Requirements for Selected Indoor Applications ............................................................................. 21 3 Definition of Terms ............................................................................................................................. 25 3.1 Disambiguation of Terms for Positioning .......................................................................................... 25 3.2 Definition of Technical Terms ................................................................................................................ 27 3.3 The Basic Measuring Principles ............................................................................................................. 29 3.4 Positioning Methods ................................................................................................................................... 31 4 Cameras .................................................................................................................................................. 34 4.1 Reference from 3D Building Models .................................................................................................... 35 4.2 Reference from Images .............................................................................................................................. 36 4.3 Reference from Deployed Coded Targets .......................................................................................... 37 4.4 Reference from Projected Targets ........................................................................................................ 38 4.5 Systems without Reference ..................................................................................................................... 39 4.6 Reference from Other Sensors ............................................................................................................... 40 4.7 Summary on Camera Based Indoor Positioning Systems ........................................................... 40 5 Infrared ................................................................................................................................................... 42 5.1 Active Beacons .............................................................................................................................................. 42 5.2 Imaging of Natural Infrared Radiation ............................................................................................... 43 5.3 Imaging of Artificial Infrared Light ....................................................................................................... 43 5.4 Summary on Infrared Indoor Positioning Systems ....................................................................... 44

Indoor Positioning Technologies

We present a new method to accurately locate persons indoors by fusing inertial navigation system (INS) techniques with active RFID technology. A foot-mounted inertial measuring units (IMUs)-based position estimation method, is aided by the received signal strengths (RSSs) obtained from several active RFID tags placed at known locations in a building. In contrast to other authors that integrate IMUs and RSS with a loose Kalman filter (KF)-based coupling (by using the residuals of inertial- and RSS-calculated positions), we present a tight KF-based INS/RFID integration, using the residuals between the INS-predicted reader-to-tag ranges and the ranges derived from a generic RSS path-loss model. Our approach also includes other drift reduction methods such as zero velocity updates (ZUPTs) at foot stance detections, zero angular-rate updates (ZARUs) when the user is motionless, and heading corrections using magnetometers. A complementary extended Kalman filter (EKF), throughout its 15-element error state vector, compensates the position, velocity and attitude errors of the INS solution, as well as IMU biases. This methodology is valid for any kind of motion (forward, lateral or backward walk, at different speeds), and does not require an offline calibration for the user gait. The integrated INS+RFID methodology eliminates the typical drift of IMU-alone solutions (approximately 1% of the total traveled distance), resulting in typical positioning errors along the walking path (no matter its length) of approximately 1.5 m.

/pdf/accurate-pedestrian-indoor-navigation-by-tightly-coupling-1bj85o1lov.pdf

Accurate Pedestrian Indoor Navigation by Tightly Coupling Foot-Mounted IMU and RFID Measurements

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

The estimation of the position of a person in a building is a must for creating Intelligent Spaces. State-of-the-art Local Positioning Systems (LPS) require a complex sensornetwork infrastructure to locate with enough accuracy and coverage. Alternatively, Inertial Measuring Units (IMU) can be used to estimate the movement of a person; a methodology that is called Pedestrian Dead-Reckoning (PDR). In this paper, we describe and implement a Kalman-based framework, called INS-EKF-ZUPT (IEZ), to estimate the position and attitude of a person while walking. IEZ makes use of an Extended Kalman filter (EKF), an INS mechanization algorithm, a Zero Velocity Update (ZUPT) methodology, as well as, a stance detection algorithm. As the IEZ methodology is not able to estimate the heading and its drift (non-observable variables), then several methods are used for heading drift reduction: ZARU, HDR and Compass. The main contribution of the paper is the integration of the heading drift reduction algorithms into a Kalman-based IEZ platform, which represents an extended PDR methodology (IEZ+) valid for operation in indoor spaces with local magnetic disturbances. The IEZ+ PDR methodology was tested in several simulated and real indoor scenarios with a low-performance IMU mounted on the foot. The positioning errors were about 1% of the total travelled distance, which are good figures if compared with other works using IMUs of higher performance.

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Indoor pedestrian navigation using an INS/EKF framework for yaw drift reduction and a foot-mounted IMU

Local positioning systems (LPSs) based on acoustic transducers (mainly ultrasonic) offer accurate localization in indoor environments. However, their performance is commonly limited by the transducers' frequency bandwidth and emission pattern. 3D-LOCUS is a new advanced acoustic LPS that claims subcentimeter accuracy even in turbulent environments. This is achieved through the use of broadband omnidirectional transducers, suitable design of emitted signals, proper calibration, and bidirectional emissions. This paper evaluates the 3D-LOCUS LPS by providing results in terms of some positioning metrics such as accuracy, resolution, and coverage, which show how it outperforms other state-of-the-art LPS prototypes. 3D-LOCUS operating in bidirectional mode minimizes environmental effects, such as temperature and airflows, attaining localization errors below 9 mm with a 90% confidence level, an RMS error below 7.4 mm, and 5 mm resolution in a localization volume of 2 times 2 times 0.4 m3.

Performance Evaluation of 3D-LOCUS Advanced Acoustic LPS

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).

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

Cellular ferroelectrets exhibit interesting electromechanical- acoustical characteristics. Their recent appearance and remarkable properties open up new possibilities for the design and development of ultrasonic transducers. In particular, the feasibility of fabricating ultrasonic vortex generators using ferroelectret films is demonstrated in this work. To this end, a transducer prototype was built by gluing the material onto a tangential-helical surface (outer diameter: 40 mm, pitch: 3.45 mm). Experimental results agree well with the theoretical estimation of the pressure and phase of the acoustic field in the near field and far field, which corroborates the potential of ferroelectrets to customize special acoustic fields. Furthermore, the proposed fabrication procedure is inexpensive and represents a new alternative for exploring and analyzing the special characteristics of acoustical helical wavefronts.

/pdf/airborne-ultrasonic-vortex-generation-using-flexible-2lf4cnrob7.pdf

Airborne ultrasonic vortex generation using flexible ferroelectrets

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

J. C. Prieto

Papers

Indoor pedestrian navigation using an INS/EKF framework for yaw drift reduction and a foot-mounted IMU

Performance Evaluation of 3D-LOCUS Advanced Acoustic LPS

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

Airborne ultrasonic vortex generation using flexible ferroelectrets

PDR with a Foot-Mounted IMU and Ramp Detection