A main problem in autonomous vehicles in general, and in Unmanned Aerial Vehicles (UAVs) in particular, is the determination of the attitude angles. A novel method to estimate these angles using off-the-shelf components is presented. This paper introduces an Attitude Heading Reference System (AHRS) based on the Unscented Kalman Filter (UKF) using the Three Axis Attitude Determination (TRIAD) algorithm as the observation model. The performance of the method is assessed through simulations and compared to an AHRS based on the Extended Kalman Filter (EKF). The paper presents field experiment results using a real fixed-wing UAV. The results show good real-time performance with low computational cost in a microcontroller.

In recent years, indoor localization systems have been the object of significant research activity and of growing interest for their great expected social impact and their impressive business potential. Application areas include tracking and navigation, activity monitoring, personalized advertising, Active and Assisted Living (AAL), traceability, Internet of Things (IoT) networks, and Home-land Security. In spite of the numerous research advances and the great industrial interest, no canned solutions have yet been defined. The diversity and heterogeneity of applications, scenarios, sensor and user requirements, make it difficult to create uniform solutions. From that diverse reality, a main problem is derived that consists in the lack of a consensus both in terms of the metrics and the procedures used to measure the performance of the different indoor localization and navigation proposals. This paper introduces the general lines of the EvAAL benchmarking framework, which is aimed at a fair comparison of indoor positioning systems through a challenging competition under complex, realistic conditions. To evaluate the framework capabilities, we show how it was used in the 2016 Indoor Positioning and Indoor Navigation (IPIN) Competition. The 2016 IPIN competition considered three different scenario dimensions, with a variety of use cases: (1) pedestrian versus robotic navigation, (2) smartphones versus custom hardware usage and (3) real-time positioning versus off-line post-processing. A total of four competition tracks were evaluated under the same EvAAL benchmark framework in order to validate its potential to become a standard for evaluating indoor localization solutions. The experience gained during the competition and feedback from track organizers and competitors showed that the EvAAL framework is flexible enough to successfully fit the very different tracks and appears adequate to compare indoor positioning systems.

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As the number of wind power applications with power electronic interfaces in the grid increases, it is becoming unacceptable to disconnect the generating units every time disturbances occur, especially under voltage dips, as was a common practice in the past. Keeping the converter online during unbalanced voltage, and guaranteeing the actual standards of the converter connected to the grid, is becoming a very critical issue. From these goals, the design of a robust back-to-back neutral point clamped (three levels) voltage source converter of 150 kVA is developed in this paper. The converter is divided into two main parts: the power electronic system and the control electronic system. Concerning the first part, on the one hand, the paper presents the designs of active and passive components as insulated gate bipolar transistor, free-wheeling diodes, clamping diodes, grid filter, dc-bus capacitors, etc.; and on the other hand, the converter requirements are analyzed to ride through real grid conditions, i.e., unbalanced voltage dips. Concerning the control electronic system, the chosen electronic structure and the task distribution between the two processors used are shown.Index Terms-Back-to-back neutral point clamped (NPC) converter, dc-bus capacitors, DSP and field-programmable gate array (FPGA), local control level (LCL) filter, voltage dip.

The predicted growth of urban populations has prompted researchers and administrations to improve services provided to citizens. At the heart of these services are wireless networks of multiple different sensors supported by the Internet of Things. The main purpose of these networks is to provide sufficient information to achieve more intelligent transport, energy supplies, social services, public environments (indoor and outdoor) and security, etc. Two major technological advances would improve such networks in Smart Cities: efficient communication between nodes and a reduction in each node's power consumption. The present paper analyses how event-based sampling techniques can address both challenges. We describe the fundamentals of the triggering mechanisms that characterise Send-on-Delta, Send-on-Area, Send-on-Energy and Send-on-Prediction techniques to restrict the number of transmissions between the sensor node and the supervision or monitoring node without degrading tracking of the sensed variable. At the same time, these aperiodic techniques reduce consumption by sensor node electronic devices. In order to quantify the energy savings, we evaluate the increase achieved in the average lifetime of sensor node batteries. The data provided by Smart City tools in the city of Santander (Spain) were selected to conduct a case study of the main pollutants that determine city air quality: SO 2 , NO 2 , O 3 and PM 10 . We conclude that event-based sensing techniques can yield up to 50% savings in sensor node consumption compared to classical periodic sensing techniques.INDEX TERMS Air quality monitoring, event-based sampling, sensor energy saving, smart cities technologies, wireless sensor network.

Image transmission using incoherent optical fiber bundles (IOFBs) requires prior calibration to obtain the spatial in-out fiber correspondence necessary to reconstruct the image captured by the pseudo-sensor. This information is recorded in a Look-Up Table called the Reconstruction Table (RT), used later for reordering the fiber positions and reconstructing the original image. This paper presents a very fast method based on image-scanning using spaces encoded by a weighted binary code to obtain the in-out correspondence. The results demonstrate that this technique yields a remarkable reduction in processing time and the image reconstruction quality is very good compared to previous techniques based on spot or line scanning, for example.

Navigation employing low cost MicroElectroMechanical Systems (MEMS) sensors in Unmanned Aerial Vehicles (UAVs) is an uprising challenge. One important part of this navigation is the right estimation of the attitude angles. Most of the existent algorithms handle the sensor readings in a fixed way, leading to large errors in different mission stages like take-off aerobatic maneuvers. This paper presents an adaptive method to estimate these angles using off-the-shelf components. This paper introduces an Attitude Heading Reference System (AHRS) based on the Unscented Kalman Filter (UKF) using the Fast Optimal Attitude Matrix (FOAM) algorithm as the observation model. The performance of the method is assessed through simulations. Moreover, field experiments are presented using a real fixed-wing UAV. The proposed low cost solution, implemented in a microcontroller, shows a satisfactory real time performance.

This paper describes the theoretical and practical foundations for remote control of a mobile robot for nonlinear trajectory tracking using an external localisation sensor. It constitutes a classical networked control system, whereby event-based techniques for both control and state estimation contribute to efficient use of communications and reduce sensor activity. Measurement requests are dictated by an event-based state estimator by setting an upper bound to the estimation error covariance matrix. The rest of the time, state prediction is carried out with the Unscented transformation. This prediction method makes it possible to select the appropriate instants at which to perform actuations on the robot so that guidance performance does not degrade below a certain threshold. Ultimately, we obtained a combined event-based control and estimation solution that drastically reduces communication accesses. The magnitude of this reduction is set according to the tracking error margin of a P3-DX robot following a nonlinear trajectory, remotely controlled with a mini PC and whose pose is detected by a camera sensor.