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The DB built through proposed method (a), and the reference DB built by using the conventional floor plan aided training approach (b). WiFi fingerprinting error distribution when using proposed DB (a) and using reference DB (b). Abstract This paper presents a method that trains the WiFi fingerprint database using sensor-based navigation solutions. Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Loran is a radio-based navigation system originally designed for naval applications. Abstract In an inertial sensor-based pedestrian navigation system, the position is estimated by double integrating external acceleration.
Abstract Autonomous mobile robots have become a very popular and interesting topic in the last decade.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers.
Abstract We present a waist-worn personal navigation system based on inertial measurement units. This supports the scenario in which a user quickly scans the first paragraph and decides to move on to other areas of the site. This navigation bar, which appears at the bottom of the page, demonstrates an interesting blend of graphic icons (with labels) and textual options. This pull-down menu enables users to select a location without first going to a separate web page.
Since micro-electromechanical systems (MEMS) sensors provide only a short-term accuracy but suffer from the accuracy degradation with time, we restrict the time length of available indoor navigation trajectories, and conduct post-processing to improve the sensor-based navigation solution. We show that Loran-C’s high-power and high repeatable accuracy are fantastic for security applications.

Height Compensation Using Ground Inclination Estimation in Inertial Sensor-Based Pedestrian Navigation. Each of them are equipped with various types of sensors such as GPS, camera, infrared and ultrasonic sensors.
The peak near to zero corresponds to straight walking, while the peak to the left corresponds to turning steps. The global navigation icons provide a splash of color, while their labels ensure usability.
It's a relatively elegant implementation, but it limits screen real estate and disables basic navigation features. Different middle-term navigation trajectories that move in and out of an indoor area are combined to make up the database. First, we show how to derive a precise location tag—with a sensitivity of about 20 meters—that is difficult to project to an exact location. We describe improved algorithms, based on detailed description of the heel strike biomechanics and its translation to accelerations of the body waist to estimate the periods of zero velocity, the step length, and the heading estimation. The textual local navigation options allow for the creation of many footer navigation bars without restrictive costs.
Furthermore, we evaluate the effect of WiFi database shifts on WiFi fingerprinting using the database generated by the proposed method. A device can use our location tag to block or allow certain actions, without knowing its precise location. The experimental results show that we are able to support pedestrian navigation with the high-resolution positioning required for most applications.
Results show that the fingerprinting errors will not increase linearly according to database (DB) errors in smartphone-based WiFi fingerprinting applications. To ensure that our tag is reproducible we make use of fuzzy extractors, a mechanism originally designed for biometric authentication.

Using this road inclination angle, height difference of one walking step is estimated and this estimation is used to reduce height error. Therefore, the integration of sensor fusion will help to solve this dilemma and enhance the overall performance.
We build a fuzzy extractor specifically designed for radio-type errors and give experimental evidence to show its effectiveness.
Through walking experiments on roads with different inclination angles, the usefulness of the proposed algorithm is verified. This paper presents a collision free mobile robot navigation based on the fuzzy logic fusion model. Eight distance sensors and a range finder camera are used for the collision avoidance approach where three ground sensors are used for the line or path following approach.
For example, an observer cannot predict the location tag inside a guarded data center from a few hundreds of meters away. The fuzzy system is composed of nine inputs which are the eight distance sensors and the camera, two outputs which are the left and right velocities of the mobile robot’s wheels, and 24 fuzzy rules for the robot’s movement. As an application, consider a location-aware disk drive that will only work inside the data center. An attacker who steals the device and is capable of spoofing Loran-C signals, still cannot make the device work since he does not know what location tag to spoof.
The proposed methodology, which includes the collision avoidance based on fuzzy logic fusion model and line following robot, has been implemented and tested through simulation and real time experiments.
Various scenarios have been presented with static and dynamic obstacles using one robot and two robots while avoiding obstacles in different shapes and sizes.

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