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

Microelectromechanical Systems (MEMS) technology is playing a key role in the design of the new generation of smartphones. Thanks to their reduced size, reduced power consumption, MEMS sensors can be embedded in above mobile devices for increasing their functionalities. However, MEMS cannot allow accurate autonomous location without external updates, e.g., from GPS signals, since their signals are degraded by various errors. When these sensors are fixed on the user's foot, the stance phases of the foot can easily be determined and periodic Zero velocity UPdaTes (ZUPTs) are performed to bound the position error. When the sensor is in the hand, the situation becomes much more complex. First of all, the hand motion can be decoupled from the general motion of the user. Second, the characteristics of the inertial signals can differ depending on the carrying modes. Therefore, algorithms for characterizing the gait cycle of a pedestrian using a handheld device have been developed. A classifier able to detect motion modes typical for mobile phone users has been designed and implemented. According to the detected motion mode, adaptive step detection algorithms are applied. Success of the step detection process is found to be higher than 97% in all motion modes.

/pdf/motion-mode-recognition-and-step-detection-algorithms-for-y4a6jei3dx.pdf

Motion Mode Recognition and Step Detection Algorithms for Mobile Phone Users

In this paper a novel step length model using a handheld Micro Electrical Mechanical System (MEMS) is presented. It combines the user's step frequency and height with a set of three parameters for estimating step length. The model has been developed and trained using 12 different subjects: six men and six women. For reliable estimation of the step frequency with a handheld device, the frequency content of the handheld sensor's signal is extracted by applying the Short Time Fourier Transform (STFT) independently from the step detection process. The relationship between step and hand frequencies is analyzed for different hand's motions and sensor carrying modes. For this purpose, the frequency content of synchronized signals collected with two sensors placed in the hand and on the foot of a pedestrian has been extracted. Performance of the proposed step length model is assessed with several field tests involving 10 test subjects different from the above 12. The percentages of error over the travelled distance using universal parameters and a set of parameters calibrated for each subject are compared. The fitted solutions show an error between 2.5 and 5% of the travelled distance, which is comparable with that achieved by models proposed in the literature for body fixed sensors only.

https://hal.archives-ouvertes.fr/hal-00878552/document

Step length estimation using handheld inertial sensors.

This paper presents a waist-worn Pedestrian Dead Reckoning (PDR) System that requires minimal end-user calibration. The PDR system is based on an Inertial Measurement Unit (IMU) comprising of a tri-axial accelerometer, a tri-axial magnetometer and a tri-axial gyroscope. We propose a novel heading estimation scheme using a quaternion-based extended Kalman filter (EKF) that estimates magnetic disturbances and corrects for them. Accelerometer measurements are used to detect step events and to estimate step lengths. Experimental results show that a relative distance error of about 3% to 8% can be obtained using our methods.

/pdf/strap-down-pedestrian-dead-reckoning-system-4vh3esv046.pdf

Strap-down Pedestrian Dead-Reckoning system

We propose a walking distance estimation method based on an adaptive step-length estimator at various walking speeds using a smartphone. First, we apply a fast Fourier transform (FFT)-based smoother on the acceleration data collected by the smartphone to remove the interference signals. Then, we analyze these data using a set of step-detection rules in order to detect walking steps. Using an adaptive estimator, which is based on a model of average step speed, we accurately obtain the walking step length. To evaluate the accuracy of the proposed method, we examine the distance estimation for four different distances and three speed levels. The experimental results show that the proposed method significantly outperforms conventional estimation methods in terms of accuracy.

https://www.mdpi.com/1424-8220/16/9/1423/pdf

Step-Detection and Adaptive Step-Length Estimation for Pedestrian Dead-Reckoning at Various Walking Speeds Using a Smartphone

A comparison between different step length estimation algorithms for pedestrian dead reckoning is presented. This work covers theoretic evaluation of the estimators' performance and presents a comparison based on measurement data. Measurement data were taken from a group of five adults walking at three different velocities. For reference, the sensors were placed according to the recommendation given for each algorithm. In respect to everyday usability the performance of the estimators is furthermore evaluated for arbitrary placement of the sensors, as it is the case when using a mobile measurement platform like a smartphone.

Comparison and evaluation of acceleration based step length estimators for handheld devices

Driver assistance systems significantly increase the driving comfort and can prevent accidents. On the other side, they require high performance and computations need to adopt to the environment to reach their goals. Reconfigurable architectures offer the requested flexibility and performance, if care is taken to partition the tasks in hardware and software parts. Elsewise, real-time requirements can't be fulfilled. In this paper, the implementation and a hardware/software partitioning of a driver assistance system for a reconfigurable architecture are presented. The assistance system detects taillights of ahead moving vehicles in dark environments or in tunnels and visualizes the results. A detailed description of the implementation on a reconfigurable platform is given. Furthermore, the experimental results demonstrate the effectiveness of the approach.

Reconfigurable HW/SW Architecture of a Real-Time Driver Assistance System

A derivation of phase smoothing for a pseudolite system is given. The distinction to phase smoothing known from literature arises from the setup of the system. As of the employed free-running pseudolites, the main challenge is to properly estimate the pseudolite clocks. The better the pseudolite clocks are estimated, the more precise are the phase measurements and the more effective is phase smoothing. Two different methods for clock estimation are examined in terms of their applicability to phase smoothing. Simulated as well as real data are used in order to evaluate the precision gain by phase smoothing. Optimal solutions according to system restrictions are presented.

Ulrich Batzer

Papers

Comparison and evaluation of acceleration based step length estimators for handheld devices

Reconfigurable HW/SW Architecture of a Real-Time Driver Assistance System

Phase smoothing in a virtually synchronized pseudolite system using stochastic clock modelling