Design of the high field side antenna of the new reflectometric system for plasma position estimate in RFXmod2

A reflectometric system will be installed in the RFX-mod2 experiment, consisting of 4 couples of transmitting/receiving antennas working in the range 16–26.5 GHz in X-mode wave propagation for tokamak discharges. They will be placed within dedicated plasma accesses in the same poloidal section at 4 equispaced poloidal positions, two on the equatorial plane, High Field Side (HFS)/Low Field Side (LFS), and two at the vertical top/bottom ports. This configuration was conceived to perform plasma position control experiments without using the magnetic measurement signals. While the accesses in LFS, top and bottom positions will accommodate pyramidal antennas, the strict room constraints in the HFS position required a special routing of the feeding waveguide and the design of a different type of antenna, described in the paper. The horn reflector (also named hoghorn) type was preferred which allows radiating (and receiving) a beam at a 90° direction with respect to the horn axis, which will be perpendicular to the equatorial plane. After fixing a reference working frequency f = 21 GHz (wavelength λ = 14.3 mm), an antenna fitting the available room was designed by means of the COMSOL Multiphysics Radio Frequency module. Four different versions were developed by introducing some modifications of the aperture shape to study their effect on the antenna performance. FEM analyses were run for frequencies in the 17–26 GHz interval to characterize the frequency response in terms of radiative patterns of the total and far electric field. The directivity of the antennae was also evaluated. The 4 versions exhibited comparable responses and the observed beam directional properties at the expected plasma distance were considered acceptable for the development of this application. A prototype of the antenna has been realized by additive manufacturing process.

Performance Evaluation of GPS Auto-Surveying Techniques

With the increase in the widespread use of Global Navigation Satellite Systems (GNSS), increasing numbers of applications require precise position data. Of all the GNSS positioning methods, the most precise are those that are based in differential systems, such as Differential GNSS (DGNSS) and Real-Time Kinematics (RTK). However, for absolute positioning, the precision of these methods is tied to their reference position estimates. With the goal of quickly auto-surveying the position of a base station receiver, four positioning methods are analyzed and compared, namely Least Squares (LS), Weighted Least Squares (WLS), Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), using only pseudorange measurements, as well as the Hatch Filter and position thresholding. The research results show that the EKF and UKF present much better mean errors than LS and WLS, with an attained precision below 1 m after about 4 h of auto-surveying. The methods that presented the best results are then tested against existing implementations, showing them to be very competitive, especially considering the differences between the used receivers. Finally, these results are used in a DGNSS test, which verifies a significant improvement in the position estimate as the base station position estimate improves.

Cognitive, Systems, and Computational Neurosciences of the Self in Motion

Navigating by path integration requires continuously estimating one's self-motion. This estimate may be derived from visual velocity and/or vestibular acceleration signals. Importantly, these senses in isolation are ill-equipped to provide accurate estimates, and thus visuo-vestibular integration is an imperative. After a summary of the visual and vestibular pathways involved, the crux of this review focuses on the human and theoretical approaches that have outlined a normative account of cue combination in behavior and neurons, as well as on the systems neuroscience efforts that are searching for its neural implementation. We then highlight a contemporary frontier in our state of knowledge: understanding how velocity cues with time-varying reliabilities are integrated into an evolving position estimate over prolonged time periods. Further, we discuss how the brain builds internal models inferring when cues ought to be integrated versus segregated—a process of causal inference. Lastly, we suggest that the study of spatial navigation has not yet addressed its initial condition: self-location. Expected final online publication date for the Annual Review of Psychology, Volume 73 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Evaluation of Multi-Sensor Fusion Methods for Ultrasonic Indoor Positioning

Indoor positioning systems have become a feasible solution for the current development of multiple location-based services and applications. They often consist of deploying a certain set of beacons in the environment to create a coverage volume, wherein some receivers, such as robots, drones or smart devices, can move while estimating their own position. Their final accuracy and performance mainly depend on several factors: the workspace size and its nature, the technologies involved (Wi-Fi, ultrasound, light, RF), etc. This work evaluates a 3D ultrasonic local positioning system (3D-ULPS) based on three independent ULPSs installed at specific positions to cover almost all the workspace and position mobile ultrasonic receivers in the environment. Because the proposal deals with numerous ultrasonic emitters, it is possible to determine different time differences of arrival (TDOA) between them and the receiver. In that context, the selection of a suitable fusion method to merge all this information into a final position estimate is a key aspect of the proposal. A linear Kalman filter (LKF) and an adaptive Kalman filter (AKF) are proposed in that regard for a loosely coupled approach, where the positions obtained from each ULPS are merged together. On the other hand, as a tightly coupled method, an extended Kalman filter (EKF) is also applied to merge the raw measurements from all the ULPSs into a final position estimate. Simulations and experimental tests were carried out and validated both approaches, thus providing average errors in the centimetre range for the EKF version, in contrast to errors up to the meter range from the independent (not merged) ULPSs.

Vehicle Position Tracking Using Kalman Filter And CWLS Optimization

Vehicle position estimation for wireless network has been studied in many fields since it has the ability to provide a variety of services, such as detecting oncoming collisions and providing warning signals to alert the driver. The services provided are often based on collaboration among vehicles that are equipped with relatively simple motion sensors and GPS units. Awareness of its precise position is vital to every vehicle, so that it can provide accurate data to its peers. Currently, typical positioning techniques integrate GPS receiver data and measurements of the vehicles motion. However, when the vehicle passes through an environment that creates multipath effect, these techniques fail to produce high position accuracy that they attain in open environments. Unfortunately, vehicles often travel in environments that cause multipath effect, such as areas with high buildings, trees, or tunnels. The goal of this research is to minimize the multipath effect with respect to the position accuracy of vehicles. The proposed technique first detects whether there is disturbance in the vehicle position estimate that is caused by the multipath effect using hypothesis test. This technique integrates all information with the vehicle's own data and the Constrained Weighted Least Squares (CWLS) optimization approach with time difference of arrival (TDOA) technique and minimizes the position estimate error of the vehicle. Kalman filter is used for smoothing range data and mitigating the NLOS errors. The positioning problem is formulated in a state-space framework and the constraints on system states are considered explicitly. The proposed recursive positioning algorithm will be comparatively more robust to measurement errors because it updates the technique that feeds the position corrections back to the Kalman Filter as compared with a Kalman tracking algorithm that estimates the target track directly from the TDOA measurements. It compensates the GPS data and decreases random error influence to the position precision. The new techniques presented in this thesis decrease the error in the position estimate. Simulation results show that the proposed tracking algorithm can improve the accuracy significantly.

Vehicle Position Tracking Using Kalman Filter And CWLS Optimization

Vehicle position estimation for wireless network has been studied in many fields since it has the ability to provide a variety of services, such as detecting oncoming collisions and providing warning signals to alert the driver. The services provided are often based on collaboration among vehicles that are equipped with relatively simple motion sensors and GPS units. Awareness of its precise position is vital to every vehicle, so that it can provide accurate data to its peers. Currently, typical positioning techniques integrate GPS receiver data and measurements of the vehicles motion. However, when the vehicle passes through an environment that creates multipath effect, these techniques fail to produce high position accuracy that they attain in open environments. Unfortunately, vehicles often travel in environments that cause multipath effect, such as areas with high buildings, trees, or tunnels. The goal of this research is to minimize the multipath effect with respect to the position accuracy of vehicles. The proposed technique first detects whether there is disturbance in the vehicle position estimate that is caused by the multipath effect using hypothesis test. This technique integrates all information with the vehicle's own data and the Constrained Weighted Least Squares (CWLS) optimization approach with time difference of arrival (TDOA) technique and minimizes the position estimate error of the vehicle. Kalman filter is used for smoothing range data and mitigating the NLOS errors. The positioning problem is formulated in a state-space framework and the constraints on system states are considered explicitly. The proposed recursive positioning algorithm will be comparatively more robust to measurement errors because it updates the technique that feeds the position corrections back to the Kalman Filter as compared with a Kalman tracking algorithm that estimates the target track directly from the TDOA measurements. It compensates the GPS data and decreases random error influence to the position precision. The new techniques presented in this thesis decrease the error in the position estimate. Simulation results show that the proposed tracking algorithm can improve the accuracy significantly.

Drone vs. Bird Detection: Deep Learning Algorithms and Results from a Grand Challenge

Adopting effective techniques to automatically detect and identify small drones is a very compelling need for a number of different stakeholders in both the public and private sectors. This work presents three different original approaches that competed in a grand challenge on the “Drone vs. Bird” detection problem. The goal is to detect one or more drones appearing at some time point in video sequences where birds and other distractor objects may be also present, together with motion in background or foreground. Algorithms should raise an alarm and provide a position estimate only when a drone is present, while not issuing alarms on birds, nor being confused by the rest of the scene. In particular, three original approaches based on different deep learning strategies are proposed and compared on a real-world dataset provided by a consortium of universities and research centers, under the 2020 edition of the Drone vs. Bird Detection Challenge. Results show that there is a range in difficulty among different test sequences, depending on the size and the shape visibility of the drone in the sequence, while sequences recorded by a moving camera and very distant drones are the most challenging ones. The performance comparison reveals that the different approaches perform somewhat complementary, in terms of correct detection rate, false alarm rate, and average precision.

Safe Navigation and Visual Odometry-based Localization for Planetary Exploration Rovers

<p>The future robotic exploration of planetary surfaces will require autonomous and safe operations to accomplish outstanding scientific objectives. The main goal of space robotic systems consists in expanding our access capability to harsh environments in the solar system (<em>e.g.</em>, Martian polar caps, icy moons). However, the operations of systems onboard landers and rovers are still mainly commanded and controlled by ground operators. To enhance the efficiency of future rovers, we are developing a robust guidance, navigation and control system that enables safe mobility on different terrain and slopes conditions, including the presence of obstacles.</p><p>High slippery terrains, such as sandy-loose soils, could prevent the rover locomotion, affecting its safety. Furthermore, the presence of these demanding terrains may impact on the rover navigation, leading to inaccuracies in the Wheel Odometry (WO) measurements because of wheels’ loss of traction. Therefore, we implemented a navigation algorithm based on Visual Odometry (VO) that is the technique based on the processing of stereo-camera images captured at successive times during the vehicle’s motion. This method is fundamental to help WO during operations that require fast responses and high-accurate positioning. We also adopted a LIDAR sensor to improve the position estimate accuracy by processing measurements associated with well-known terrain features.</p><p>We present here numerical simulations of rover navigation across different terrain conditions by using accurate dynamical models, including the deformability of both wheel and terrain. VO and LIDAR data are simulated and processed to determine the positioning accuracies that enable safe navigation. The results are in full agreement with the existing (<em>i.e.</em>, Mars Exploration Rovers (MER)) and future (<em>i.e.</em>, ExoMars) rover performances. Our algorithm allows reconstructing the rover trajectory with higher accuracies compared to the localization system requirement of the NASA MER rovers (<em>i.e.</em>, 10% error over 100 meters traverse).</p>

An Indoor Robust Localization Algorithm Based on Data Association Technique

As a key technology of the Internet of Things, wireless sensor network (WSN) has been used widely in indoor localization systems. However, when the sensor is transmitting signals, it is affected by the non-line-of-sight (NLOS) transmission, and the accuracy of the positioning result is decreased. Therefore, solving the problem of NLOS positioning has become a major focus for indoor positioning. This paper focuses on solving the problem of NLOS transmission that reduces positioning accuracy in indoor positioning. We divided the anchor nodes into several groups and obtained the position information of the target node for each group through the maximum likelihood estimation (MLE). By identifying the NLOS method, a part of the position estimates polluted by NLOS transmission was discarded. For the position estimates that passed the hypothesis testing, a corresponding poly-probability matrix was established, and the probability of each position estimate from line-of-sight (LOS) and NLOS was calculated. The position of the target was obtained by combining the probability with the position estimate. In addition, we also considered the case where there was no continuous position estimation through hypothesis testing and through the NLOS tracking method to avoid positioning errors. Simulation and experimental results show that the algorithm proposed has higher positioning accuracy and higher robustness than other algorithms.

Indoor Localization System Based on Bluetooth Low Energy for Museum Applications

In the last few years, indoor localization has attracted researchers and commercial developers. Indeed, the availability of systems, techniques and algorithms for localization allows the improvement of existing communication applications and services by adding position information. Some examples can be found in the managing of people and/or robots for internal logistics in very large warehouses (e.g., Amazon warehouses, etc.). In this paper, we study and develop a system allowing the accurate indoor localization of people visiting a museum or any other cultural institution. We assume visitors are equipped with a Bluetooth Low Energy (BLE) device (commonly found in modern smartphones or in a small chipset), periodically transmitting packets, which are received by geolocalized BLE receivers inside the museum area. Collected packets are provided to the locator server to estimate the positions of the visitors inside the museum. The position estimation is based on a feed-forward neural network trained by a measurement campaign in the considered environment and on a non-linear least square algorithm. We also provide a strategy for deploying the BLE receivers in a given area. The performance results obtained from measurements show an achievable position estimate accuracy below 1 m.