Experimental Results of a Differential Angle-of-Arrival Based 2D Localization Method Using Signals of Opportunity

This paper presents a study of differential AoA (Angle-of-Arrival) based 2D localization method utilizing FM radio signals (88 MHz–108 MHz) as Signals of Opportunity (SOP). Given prior knowledge of the transmitters’ position and signal characteristics, the proposed technique utilizes triangulation to localize receiver’s 2D position. Dual antenna interferometry provides the received signals’ AoA required for triangulation. Reliance on precise knowledge of antenna system’s orientation is removed by utilizing differential Angle of Arrivals (dAoAs). The 2D localization accuracy is improved by utilizing colocated transmitters, a concept proposed in this paper as supertowers. Analysis, simulation, and ground-based experiments have been presented; results showed that when the SNR (Signal-to-Noise Ratio) is higher than 45 dB, the proposed method localizes the receiver’s 2D position with an error of less than 15 m.

Fast Comparison of High-Precision Time Scales Using GNSS Receivers

Comparison of high-performance time scales generated by atomic clocks in laboratories of time and frequency metrology is usually performed by means of the Common View method. Laboratories are equipped with specialized GNSS receivers which measure the difference between a local time scale and a time scale of the selected satellite. Every receiver generates log files in CGGTTS data format to record measured differences. In order to calculate time differences recorded by two receivers, it is necessary to obtain these logs from both receivers and process them. This paper deals with automation and speeding up of these processes.

Performance Evaluation of Pedestrian Locations Based on Contemporary Smartphones

Nowadays, a Global Navigation Satellite System (GNSS) unit is embedded in nearly every smartphone. This unit allows a smartphone to detect the user’s location and motion, and it makes functions, such as navigation, tracking, and compass applications, available to the user. Therefore, the GNSS unit has become one of the most important features in modern smartphones. However, because most smartphones incorporate relatively low-cost GNSS chips, their localization accuracy varies depending on the number of accessible GNSS satellites, and it is highly dependent on environmental factors that cause interference such as forests and buildings. This research evaluated the performance of the GNSS units inside two different models of smartphones in determining pedestrian locations in different environments. The results indicate that the overall performances of the two devices were related directly to the environment, type of smartphone/GNSS chipset, and the application used to collect the information.

Simulation of GNSS Availability in Urban Environments Using a Panoramic Image Dataset

Performance of Global Navigation Satellite System (GNSS) positioning in urban environments is hindered by poor satellite availability because there are many man-made and natural objects in urban environments that obstruct satellite signals. To evaluate the availability of GNSS in cities, this paper presents a software simulation of GNSS availability in urban areas using a panoramic image dataset from Google Street View. Photogrammetric image processing techniques are applied to reconstruct fisheye sky view images and detect signal obstacles. Two comparisons of the results from the simulation and real world observation in Bangkok and Tokyo are also presented and discussed for accuracy assessment.

Gap Filling of the CALYPSO HF Radar Sea Surface Current Data through Past Measurements and Satellite Wind Observations

High frequency (HF) radar installations are becoming essential components of operational real-time marine monitoring systems. The underlying technology is being further enhanced to fully exploit the potential of mapping sea surface currents and wave fields over wide areas with high spatial and temporal resolution, even in adverse meteo-marine conditions. Data applications are opening to many different sectors, reaching out beyond research and monitoring, targeting downstream services in support to key national and regional stakeholders. In the CALYPSO project, the HF radar system composed of CODAR SeaSonde stations installed in the Malta Channel is specifically serving to assist in the response against marine oil spills and to support search and rescue at sea. One key drawback concerns the sporadic inconsistency in the spatial coverage of radar data which is dictated by the sea state as well as by interference from unknown sources that may be competing with transmissions in the same frequency band. This work investigates the use of Machine Learning techniques to fill in missing data in a high resolution grid. Past radar data and wind vectors obtained from satellites are used to predict missing information and provide a more consistent dataset.

The Use of the Total Electron Content Measured by Navigation Satellites to Estimate Ionospheric Conditions

Measurements of delays of the signals radiated by transmitters of navigational satellites allow us to obtain the total electron content (TEC). In addition, measurements of TEC allow solving problems such as development of local, regional, and global models of TEC and correction of ionospheric delay for increasing accuracy of positioning. Now, it is possible to set the task of calculation of critical frequency foF2 with the use of experimental values of TEC in a global scale. For this purpose it is necessary to know an equivalent slab thickness of the ionosphere τ which is a coefficient of proportionality between TEC and a maximum density of the ionosphere. The present paper is devoted to the analysis of investigation and utilization of this parameter. It is shown that (1) existing models of τ are not empirical and not always can provide an adequate accuracy of foF2 calculation, (2) experimental median τ(med) provides much larger accuracy of foF2 calculation than the empirical model and variations from day to day and allows filling gaps in the ionosonde data, and (3) it is possible to use a hyperbolic approximation and coefficient K(τ) for development of a global model of τ.

Estimation of Scintillation Indices: A Novel Approach Based on Local Kernel Regression Methods

We present a comparative study of computational methods for estimation of ionospheric scintillation indices. First, we review the conventional approaches based on Fourier transformation and low-pass/high-pass frequency filtration. Next, we introduce a novel method based on nonparametric local regression with bias Corrected Akaike Information Criteria (AICC). All methods are then applied to data from the Norwegian Regional Ionospheric Scintillation Network (NRISN), which is shown to be dominated by phase scintillation and not amplitude scintillation. We find that all methods provide highly correlated results, demonstrating the validity of the new approach to this problem. All methods are shown to be very sensitive to filter characteristics and the averaging interval. Finally, we find that the new method is more robust to discontinuous phase observations than conventional methods.

Development of a Low-Level Control System for the ROV Visor3

This paper addresses the development of the simulation of the low-level control system for the underwater remotely operated vehicle Visor3. The 6-DOF mathematical model of Visor3 is presented using two coordinated systems: Earth-fixed and body-fixed frames. The navigation, guidance, and control (NGC) structure is divided into three layers: the high level or the mission planner; the mid-level or the path planner; and the low level formed by the navigation and control systems. The nonlinear model-based observer is developed using the extended Kalman filter (EKF) which uses the linearization of the model to estimate the current state. The behavior of the observer is verified through simulations using Simulink®. An experiment was conducted with a trajectory that describes changes in the x and y and yaw components. To accomplish this task, two algorithms are compared: a multiloop PID and PID with gravity compensation. These controllers and the nonlinear observer are tested using the 6-DOF mathematical model of Visor3. The control and navigation systems are a fundamental part of the low-level control system that will allow Visor3’s operators to take advantage of more advanced vehicle’s capabilities during inspection tasks of port facilities, hydroelectric dams, and oceanographic research.

FFT Splitting for Improved FPGA-Based Acquisition of GNSS Signals

With modern global navigation satellite system (GNSS) signals, the FFT-based parallel code search acquisition must handle the frequent sign transitions due to the data or the secondary code. There is a straightforward solution to this problem, which consists in doubling the length of the FFTs, leading to a significant increase of the complexity. The authors already proposed a method to reduce the complexity without impairing the probability of detection. In particular, this led to a 50% memory reduction for an FPGA implementation. In this paper, the authors propose another approach, namely, the splitting of a large FFT into three or five smaller FFTs, providing better performances and higher flexibility. For an FPGA implementation, compared to the previously proposed approach, at the expense of a slight increase of the logic and multiplier resources, the splitting into three and five allows, respectively, a reduction of 40% and 64% of the memory, and of 25% and 37.5% of the processing time. Moreover, with the splitting into three FFTs, the algorithm is applicable for sampling frequencies up to 24.576 MHz for L5 band signals, against 21.846 MHz with the previously proposed algorithm. The algorithm is applied here to the GPS L5 and Galileo E5a, E5b, and E1 signals.

A DCM Based Attitude Estimation Algorithm for Low-Cost MEMS IMUs

An attitude estimation algorithm is developed using an adaptive extended Kalman filter for low-cost microelectromechanical-system (MEMS) triaxial accelerometers and gyroscopes, that is, inertial measurement units (IMUs). Although these MEMS sensors are relatively cheap, they give more inaccurate measurements than conventional high-quality gyroscopes and accelerometers. To be able to use these low-cost MEMS sensors with precision in all situations, a novel attitude estimation algorithm is proposed for fusing triaxial gyroscope and accelerometer measurements. An extended Kalman filter is implemented to estimate attitude in direction cosine matrix (DCM) formation and to calibrate gyroscope biases online. We use a variable measurement covariance for acceleration measurements to ensure robustness against temporary nongravitational accelerations, which usually induce errors when estimating attitude with ordinary algorithms. The proposed algorithm enables accurate gyroscope online calibration by using only a triaxial gyroscope and accelerometer. It outperforms comparable state-of-the-art algorithms in those cases when there are either biases in the gyroscope measurements or large temporary nongravitational accelerations present. A low-cost, temperature-based calibration method is also discussed for initially calibrating gyroscope and acceleration sensors. An open source implementation of the algorithm is also available.