The seasonal effects of deciduous tree foliage in CORS-GNSS measurements (VRS/FKP)

U ovom se članku istražuje učinkovitost i uporaba sustava prikupljanja podataka temeljenih na GNSS u sezonskim učincima lišća listopadnog drveća. Kao opće pravilo, poželjan je jasan pogled na nebo kada rabimo GNSS za određivanje položaja. Sezona u slučaju bjelogorice je parametar koji utječe na prigušenja GNSS signala. Kao rezultat ovog prigušenja, pozicije koje izračunavaju slabe signale manje su točne. To znači da je uporaba GNSS u šumi jedna od najzahtjevnijih uporaba ove tehnologije i ona koja zahtijeva posebnu pozornost prilikom ocjenjivanja GNSS prijamnika koji će se rabiti u takvom okruženju. U radu se procjenjuje GNSS položajna točnost, preciznost i učinkovitost u šumskom području. Dobivene horizontalne razlike za tri polazišta u travnju između CORS-VRS/FKP i ukupnog rezultata nadzorne stanice su ±(1÷3) cm. Dobivene visinske razlike za tri polazišta u travnju između CORS-FKP i ukupnog rezultata nadzorne stanice su ±(2÷4) cm.This article examines the performance and use of GNSS based data acquisition systems in the seasonal effects of deciduous tree foliage. As a general rule, a clear view of the sky is preferred when using GNSS for determining location. Season in the case of deciduous trees is the parameter affecting GNSS signal attenuation. As a result of this attenuation, positions computing weak signals tend to be less accurate. This means that using GNSS in forest is one of the most demanding uses of technology and one that requires particular attention when evaluating GNSS receivers that will be used in such an environment. This paper evaluates GNSS positional accuracy, precision and performance in the forest area. The obtained horizontal differences for three baselines in April between CORS-VRS/FKP and Total station survey results are ±(1÷3) cm. The height differences obtained for three baselines in April between CORS-FKP and Total station survey results are ±(2÷4) cm

Predicting Trajectory Paths For Collision Avoidance Systems

This work was motivated by the idea of developing a more encompassing collision avoidance system that supported vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications. Current systems are mostly based on line of sight sensors that are used to prevent a collision, but these systems would prevent even more accidents if they could detect possible collisions before both vehicles were in line of sight. For this research we concentrated mostly on the aspect of improving the prediction of a vehicle\u27s future trajectory, particularly on non-straight paths. Having an accurate prediction of where the vehicle is heading is crucial for the system to reliably determine possible path intersections of more than one vehicle at the same time. We first evaluated the benefits of merging Global Positioning System (GPS) data with the Geographical Information System (GIS) data to correct improbable predicted positions. We then created a new algorithm called the Dead Reckoning with Dynamic Errors (DRWDE) sensor fusion, which can predict future positions at the rate of its fastest sensor, while improving the handling of accumulated error while some of the sensors are offline for a given period of time. The last part of out research consisted in the evaluation of the use of smartphones\u27 built-in sensors to predict a vehicle\u27s trajectory, as a possible intermediate solution for a vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications, until all vehicles have all the necessary sensors and communication infrastructure to fully populate this new system. For the first part of our research, the actual experimental results validated our proposed system, which reduced the position prediction errors during curves to around half of what it would be without the use of GIS data for prediction corrections. The next improvement we worked on was the ability to handle change in noise, depending on unavailable sensor measurements, permitting a flexibility to use any type of sensor and still have the system run at the fastest frequency available. Compared to a more common KF implementation that run at the rate of its slowest sensor (1Hz in our setup), our experimental results showed that our DRWDE (running at 10Hz) yielded more accurate predictions (25-50% improvement) during abrupt changes in the heading of the vehicle. The last part of our research showed that, comparing to results obtained with the vehicle-mounted sensors, some smartphones yield similar prediction errors and can be used to predict a future position

Utilization of unmanned aerial vehicles and proximal sensing to detect Riesling vineyard variability

A single vineyard block can consist of significant spatial variability for several grape-growing attributes. The ability to detect and subsequently respond to this variation can lead to improved vineyard management, a growing practice termed precision viticulture. The overall goal of this research study was to determine if remote-sensing technologies could be used to detect Riesling vineyard variability, thus enhancing precision viticulture implementation. Approximately 80 grapevines in a grid pattern were geo-located within each of six commercial Riesling vineyards across the Niagara Peninsula in Ontario. From these grapevines the following variables were measured to determine their vineyard variation: soil and vine water status, vine size/vigor, winter hardiness, virus titer, yield components, and berry composition. Subsequently, remote-sensing technologies collected thermal [by unmanned aerial vehicle (UAV)] and multispectral (by UAV and ground-based proximal sensing technology GreenSeeker™) data from each block. Multispectral data were transformed into the Normalized Difference Vegetation Index (NDVI). Vineyard UAV NDVI maps were further used for selective harvesting of areas corresponding to low vs. high NDVI and wines made from these two zones were compared chemically and sensorially. The hypothesis was that remote and proximal sensing technologies could accurately detect vineyard variation for manually collected variables and further implicate differences in wine attributes upon zonal harvesting. Direct positive correlations were observed between remotely and proximally sensed NDVI vs. vine size, leaf stomatal conductance, leaf water potential, virus infection, yield, berry weight, and titratable acidity and inverse correlations with Brix and potentially-volatile terpene concentration. Maps created from remotely and proximally sensed data demonstrated similar spatial configurations to interpolated maps of these variables. In general, GreenSeeker NDVI demonstrated the most significant relationships with measured variables compared to UAV NDVI and UAV thermal data. Wines created from areas of low vs high NDVI differed inconsistently in their wine pH. Sensorially, in certain sites and vintages, panelists were able to distinguish between wines made from low vs high NDVI zones. Overall, remote sensing demonstrates the ability to detect vineyard areas differing in measures of vine health, size, yield, berry composition, and wine attributes, though more research is needed to understand the inconsistent results observed between vineyard sites and vintages

IF-level signal-processing of GPS and Galileo Radionavigation signals using MATLAB/Simulink®: Including Effects of Interference and Multipath

Open-source GNSS simulator models are rare and somewhat difficult to find. Therefore, Laboratory of Electronics and Communications Engineering in the former Tampere University of Technology (and now Tampere University, Hervanta Campus) has took it upon itself to develop, from time to time, a free and open-source simulator model based on MATLAB/Simulink® for signal processing of a carefully selected set of GNSS radionavigation signals, namely, Galileo E1, Galileo E5, GPS L1, and GPS L5. This M.Sc. thesis is the culmination of those years which have been spent intermittently on research and development of that simulator model. The first half of this M.Sc. thesis is a literature review of some topics which are believed to be of relevance to the thesis’s second half which is in turn more closely associated with documenting the simulator model in question. In particular, the literature review part presents the reader with a plethora of GNSS topics ranging from history of GNSS technology to characteristics of existing radionavigation signals and, last but not least, compatibility and interoperability issues among existing GNSS constellations. While referring to the GNSS theory whenever necessary, the second half is, however, mainly focused on describing the inner-workings of the simulator model from the standpoint of software implementations. Finally, the second half, and thereby the thesis, is concluded with a presentation of various statistical results concerning signal acquisition’s probabilities of detection and false-alarm, in addition to signal tracking’s RMSE

Space-partitioning with cascade-connected ANN structures for positioning in mobile communication systems

The world around us is getting more connected with each day passing by – new portable devices employing wireless connections to various networks wherever one might be. Locationaware computing has become an important bit of telecommunication services and industry. For this reason, the research efforts on new and improved localisation algorithms are constantly being performed. Thus far, the satellite positioning systems have achieved highest popularity and penetration regarding the global position estimation. In spite the numerous investigations aimed at enabling these systems to equally procure the position in both indoor and outdoor environments, this is still a task to be completed. This research work presented herein aimed at improving the state-of-the-art positioning techniques through the use of two highly popular mobile communication systems: WLAN and public land mobile networks. These systems already have widely deployed network structures (coverage) and a vast number of (inexpensive) mobile clients, so using them for additional, positioning purposes is rational and logical. First, the positioning in WLAN systems was analysed and elaborated. The indoor test-bed, used for verifying the models’ performances, covered almost 10,000m2 area. It has been chosen carefully so that the positioning could be thoroughly explored. The measurement campaigns performed therein covered the whole of test-bed environment and gave insight into location dependent parameters available in WLAN networks. Further analysis of the data lead to developing of positioning models based on ANNs. The best single ANN model obtained 9.26m average distance error and 7.75m median distance error. The novel positioning model structure, consisting of cascade-connected ANNs, improved those results to 8.14m and 4.57m, respectively. To adequately compare the proposed techniques with other, well-known research techniques, the environment positioning error parameter was introduced. This parameter enables to take the size of the test environment into account when comparing the accuracy of the indoor positioning techniques. Concerning the PLMN positioning, in-depth analysis of available system parameters and signalling protocols produced a positioning algorithm, capable of fusing the system received signal strength parameters received from multiple systems and multiple operators. Knowing that most of the areas are covered by signals from more than one network operator and even more than one system from one operator, it becomes easy to note the great practical value of this novel algorithm. On the other hand, an extensive drive-test measurement campaign, covering more than 600km in the central areas of Belgrade, was performed. Using this algorithm and applying the single ANN models to the recorded measurements, a 59m average distance error and 50m median distance error were obtained. Moreover, the positioning in indoor environment was verified and the degradation of performances, due to the crossenvironment model use, was reported: 105m average distance error and 101m median distance error. When applying the new, cascade-connected ANN structure model, distance errors were reduced to 26m and 2m, for the average and median distance errors, respectively. The obtained positioning accuracy was shown to be good enough for the implementation of a broad scope of location based services by using the existing and deployed, commonly available, infrastructure

Vehicle speed over ground radar

The non-contact speed measurement over ground (SoG) is a key component of modern vehicle technology since it allows measuring the speed of a vehicle without tapping on the wheels and can be measured without slippage. Applications can be found wherever an accurate measure of speed is needed, i.e., automatic control operations such as anti-lock braking system (ABS), Electronic stability control (ESC) or vehicle intelligent positioning system. The microwave Doppler principle is particularly suitable here because it is the least affected by environmental influences such as rain, snow, fog, temperature, wind, pollution, compared to other contactless measuring systems, i.e., Global Positioning Systems (GPS), ultrasonic, acoustics and optical sensor. The present work closes a gap in microwave SoG by developing and examining a SoG system based on a four beams radar configuration. Compared to the previous single and dual-beam measuring method, a 4-beam system is capable of estimating the speed vector of the vehicle. Furthermore, this system can minimise the effect of vehicle dynamics on the estimate of vehicle speed. Regarding the Doppler signal processing method, a distinction is made between other known estimation methods. We proposed two types of Doppler processing based on Fourier transform. Theoretical evaluation of these two methods shows that they produce an accurate estimate of mean Doppler frequency. Comparison between these two methods shows that the cross-correlation method produces more accurate estimates and can work at lower input SNR. Finally, evaluation of the developed SoG system with actual road conditions shows that the SoG system can work well on both on-road and off-road conditions with increased speed accuracy when using the cross-correlation method

Driving Manoeuvre Recognition using Mobile Sensors

Automobiles are integral in today's society as they are used for transportation, commerce, and public services. The ubiquity of automotive transportation creates a demand for active safety technologies for the consumer. Recently, the widespread use and improved sensing and computing capabilities of mobile platforms have enabled the development of systems that can measure, detect, and analyze driver behaviour. Most systems performing driver behaviour analysis depend on recognizing driver manoeuvres. Improved accuracy in manoeuvre detection has the potential to improve driving safety, through applications such as monitoring for insurance, the detection of aggressive, distracted or fatigued driving, and for new driver training. This thesis develops algorithms for estimating vehicle kinematics and recognizing driver manoeuvres using a smartphone device. A kinematic model of the car is first introduced to express the vehicle's position and orientation. An Extended Kalman Filter (EKF) is developed to estimate the vehicle's positions, velocities, and accelerations using mobile measurements from inertial measurement units and the Global Positioning System (GPS). The approach is tested in simulation and validated on trip data using an On-board Diagnostic (OBD) device as the ground truth. The 2D state estimator is demonstrated to be an effective filter for measurement noise. Manoeuvre recognition is then formulated as a time-series classification problem. To account for an arbitrary orientation of the mobile device with respect to the vehicle, a novel method is proposed to estimate the phone's rotation matrix relative to the car using PCA on the gyroscope signal. Experimental results demonstrate that e Principal Component (PC) corresponds to a frame axis in the vehicle reference frame, so that the PCA projection matrix can be used to align the mobile device measurement data to the vehicle frame. A major impediment to classifier-manoeuvre recognition is the need for training data, specifically collecting enough data and generating an accurate ground truth. To address this problem, a novel training process is proposed to train the classifier using only simulation data. Training on simulation data bypasses these two issues as data can be cheaply generated and the ground truth is known. In this thesis, a driving simulator is developed using a Markov Decision Process (MDP) to generate simulated data for classifier training. Following training data generation, feature selection is performed using simple features such as velocity and angular velocity. A manoeuvre segmentation classifier is trained using multi-class SVMs. Validation was performed using data collected from driving sessions. A grid search was employed for parameter tuning. The classifier was found to have a 0.8158 average precision rate and a 0.8279 average recall rate across all manoeuvres resulting in an average F1 score of 0.8194 on the dataset

Innovative Location Based Scheme for Internet Security Protocol. A proposed Location Based Scheme N-Kerberos Security Protocol Using Intelligent Logic of Believes, Particularly by Modified BAN Logic.

The importance of the data authentication has resulted in the science of the data protection. Interest in this knowledge has been growing due to the increase in privacy of the user's identity, especially after the widespread use of online transactions. Many security techniques are available to maintain the privacy of the user's identity. These include password, smart card or token and face recognition or finger print. But unfortunately, the possibility to duplicate the identity of a user is still possible. Recently, specialists used the user's physical location as a new factor in order to increase the strength of the verification of the user's identity. This thesis focused on the authentication-based user's location. It is based on the idea of using the Global Position System in order to verify the user identity. Improving Kerberos protocol using GPS signal is proposed in order to eliminate the effect of replay attack. This proposal does not expect a high performance from the user during the implementation of the security system. Moreover, to give users more confidence to use security protocol, it has to be evaluated before accepting it. Thus, a measurement tool used to validate protocols called BAN logic was described. In this thesis, a new form of BAN logic which aims to raise the efficiency checking process of the protocol protection strength using the GPS signal is proposed. The proposed form of Kerberos protocol has been analysed using the new form of BAN logic. The new scheme has been tested and compared with the existing techniques to demonstrate its merits and capabilities

Underwater acoustic localisation and referencing: an enhanced subsurface positioning method for archaeological data collection of submerged cultural resources

Traditional and modern optical methods of maritime archaeological site documentation typically lack absolute spatial information as part of submerged cultural heritage surveys in locations where shore-based satellite positioning technologies are not applicable for use. This is due to the inability to use satellite positioning receivers beneath the water surface as a result of the high attenuation rate of electromagnetic waves in a marine environment. The defence and offshore energy industries solved this problem through the incorporation of acoustic ranging systems used in conjunction with satellite positioning receivers. Underwater acoustic ranging equipment, such as ultra-short baseline (USBL) and long baseline (LBL) systems, are commonly used in geophysical surveys and marine construction projects to provide subsurface positioning information of underwater instrumentation such as towed sonar arrays, remotely-operated vehicles (ROVs), and divers. Satellite positioning and underwater acoustic ranging configurations have been in continuous use for more than three decades, and such equipment systems are readily available throughout the world for commercial and scientific applications. Despite the proven effectiveness and accessibility of these systems, maritime archaeology fieldwork practices have not successfully integrated these systems into established underwater data collection techniques. This thesis was established to determine if traditional and modern optical maritime archaeological data collection techniques are capable of being supplemented by a tandem satellite positioning system and USBL acoustic ranging configuration to provide underwater positioning information in accordance with universally-accepted geophysical surveying spatial and equipment standards, such as those published by the International Hydrographic Organization (IHO), Bureau of Ocean Energy Management (BOEM), Historic England, and others. In the absence of recognised spatial standards within the maritime archaeology community, this thesis relied on geophysical surveying spatial and equipment standards as the research parameters upon which the Underwater Acoustic Localisation and Referencing (UALR) methodology was developed. The UALR methodology presented in this thesis successfully incorporated a GPS/USBL configuration for providing subsurface latitude and longitude coordinates for ground control point positions for traditional and modern optical archaeological data collection techniques. The collected datasets were georeferenced using underwater spatial information gathered by the UALR methodology process, and demonstrated that these methods are capable of achieving spatial accuracy and measurement precision in accordance with geophysical surveying specifications. By adhering to these standards, the UALR methodology is applicable for use by archaeologists in support of geophysical surveying operations throughout the world