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The accuracy of indoor positioning systems is commonly computed as a metric based on the Euclidean distance from estimated locations to actual locations. This paper suggests that positioning error distances should be computed as the lengths of the paths that a person may follow when going from wrongly estimated positions to the real positions. The paper proposes a method that calculates the paths...
All indoor positioning approaches face the challenge to deal with erroneous input data from sensors. Especially independent systems like dead reckoning rely on highly accurate input data from accelerometers and gyroscopes for an accurate prediction of the user's position. But with the widespread of affordable mobile devices equipped with low-cost sensors, the obtained input data is noisy and of poor...
Shoe-mounted inertial navigation systems, aka pedestrian dead reckoning or PDR sensors, are being preferred for pedestrian navigation because of the accuracy offered by them. Such shoe sensors are, for example, the obvious choice for real time location systems of first responders. The opensource platform OpenShoe has reported application of multiple IMUs in shoe-mounted PDR sensors to enhance noise...
Sensing the presence of people in indoor spaces allows smart systems to be aware of and responsive to the occupants, and paves the way for a wide range of applications. In this paper, we show how the reflection patterns of ultrasonic signals can be leveraged to detect the presence of still persons. We propose the use of supervised learning over segmented reflection patterns, and prove that this method...
High accuracy in indoor navigation with foot-mounted sensors attracts a lot of researchers in the last decades. Most indoor positioning schemes based on strap-down inertial navigation can only be used for normal walking. This paper present a 3D foot-mounted inertial navigation system, which can meet the challenge of the multi-gaits. During walking, the foot will have a contact with the ground in every...
Foot-mounted inertial positioning (FMIP) can face problems of inertial drifts and unknown initial states in real applications, which renders the estimated trajectories inaccurate and not obtained in a well defined coordinate system for matching trajectories of different users. In this paper, an approach adopting received signal strength (RSS) measurements for Wifi access points (APs) are proposed...
We describe a time-of-arrival-(ToA-) based localization system for smartphones. In this system, the transmitter emits modulated light-emitting diode (LED) light and sound waves, then the smartphone catches them. The smartphone measures the time of flight of sound waves and localizes its position using multilateration. The LED light is used for visible light communication, which also conveys the time...
Fingerprinting-based indoor localization systems rely on stable signal distribution characteristics of fixed signal sources for location estimation. However, indoor environments are not static and changes in the environment can lead to displacement of some signal sources, potentially causing a drop in the localization performance. It is therefore necessary to regularly monitor the signal sources and...
The public transports provide an ideal means to enable contagious diseases transmission. This paper introduces a novel idea to detect co-location of people in such environment using just the ubiquitous geomagnetic field sensor on the smartphone. Essentially, given that all passengers must share the same journey between at least two consecutive stations, we have a long window to match the user trajectory...
In this paper, we present a publicly available dataset for the evaluation of indoor positioning algorithms that use magnetic anomalies. Our dataset contains IMU and magnetometer measurements along with ground truth position measurements that have centimeter-level accuracy. To produce this dataset, we collected over 13 hours of data (51 kilometers of total distance traveled) from three different buildings,...
Reliable localization is one of the most important parts of an MAV system. Localization in an indoor GPS-denied environment is a relatively difficult problem. Current vision based algorithms track optical features to calculate odometry. We present a novel localization method which can be applied in an environment having orthogonal sets of equally spaced lines to form a grid. With the help of a monocular...
The accuracy of fingerprinting-based positioning methods accuracy is limited by the fluctuations in the radio signal intensity mainly due to reflections, refractions, and multipath interference, among other factors. We consider that the fluctuations (often modelled as a Gaussian process for simplification purposes) can be minimized by exploiting the richness of multiple signals collected simultaneously...
This paper describes an Indoor Positioning System (IPS) that fuses an Ultra-Wideband (UWB) sensor-based positioning solution with an Inertial Measurement Unit (IMU) sensor-based positioning solution to obtain a robust, yet, optimal positioning performance. Sensor fusion is accomplished via an Extended Kalman Filter (EKF) design which simultaneously estimates the IMU sensors' systematic errors and...
Pose estimation systems have seen some big developments in the last two decades due to technological advances and a greater need for these systems. The industry that thrives and simultaneously popularizes these developments is the entertainment industry. One of the latest developments in home entertainment are Virtual Reality systems that lets users have an immersive experience when playing video...
With the abundance of mobile connected devices and coexisting networks, localization solutions are inevitably subject to interference. In this paper, effects of interference on the performance of Fingerprinting Localization Algorithms (FPS) are studied both theoretically and through experimentation. The previously introduced theoretical framework based on Hypothesis Testing (HT) problem is employed...
Due to the influence of indoor signal multipath effect and human disturbance, the indoor positioning technology of WiFi fingerprint based on deep learning is poor stability. The large sample and accurate data in the room is very difficult to collect for weights training of deep learning, so it is difficult to be widely used. Firstly, the innovative algorithm with multi-sensor fingerprint and deep...
Ranked Based Fingerprinting uses only ordering indices instead of actual Wi-Fi RSS values in order to make the algorithm insensitive to devices. A key component of the RBF algorithm is a similarity measure which is used to compare and find the closest ranked fingerprints. Previous papers study a few similarity measures; here we study 49 similarity measures in a test with a benchmark with publicly...
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