Global Positioning System Sensors Research Articles

Web-based real time bridge scour monitoring system for disaster management

Natural disasters such as typhoons, earthquakes, and especially floods, often cause severe damage in Taiwan. Although bridge engineering technology has matured, the lack of an integrated bridge management system under severe disasters remains a challenge for the government. This research aims to develop a web-based system that displays the real time bridge scour information through combining Global Positioning System and Wireless Sensor Networks technologies for bridges. The system also provides early warnings for bridge safety based on a developed genetic model for estimating scour depth around the bridge piers. The bridge safety monitoring agency utilizes the system as a decision support tool to make the maintenance plan for bridges and block the bridges under severe weather conditions to prevent the damages from bridge collapse.

Application of geovisual analytics to modelling the movements of ruminants in the rural landscape using satellite tracking data

Geovisual analytics provides a framework for the development of decision support tools for landscape design, analysis and optimisation. An important application is modelling the spatial-temporal movements of ruminants and their grazing behaviour using global positioning system (GPS) collar units. This study describes the mapping and analysis of spatial distributions of animal waste products (which correlate with farm nitrogen [N] emissions) and also determination of animal feeding preferences (which correlate with animal welfare and production). Segmentation of local regions of animal N emissions provides support in meeting targets for local and international N leaching and greenhouse gas emissions. An agent-based model was used for pre-screening in order to gain insights into the clustering behaviour of sheep during feeding activities. Subsequent spatial analysis demonstrated that livestock excreta are not always randomly located, but concentrated around highly localised animal gathering points, separated by the nature of the excretion. In a separate study, the statistical significance of feeding choices was determined by testing a null hypothesis on animal boundary transitions between adjacent pastures using the binomial approximation. The analysis also included compensation for the precision of the GPS sensor, which produced a fuzzy decision boundary.

Evaluation of a novel attitude estimation algorithm for a high performance fighter aircraft

This paper discusses the evaluation of a novel attitude estimation algorithm (Kamali et al. (2013)) for a high performance fighter aircraft. This algorithm employs a new modelling approach in the Extended Kalman Filtering (EKF) framework to estimate aircraft attitude information without using forward acceleration sensor, Global Positioning System (GPS) sensors or magnetometer. Evaluation of the algorithm is conducted using flight data from a high performance fighter aircraft and using flight simulation data. Estimation results during various manoeuvres such as full rolls, inverted loops, split-S manoeuvres, steep climbs, and dives, are studied. Effect of different wind perturbations (gust, shear, turbulence, etc.), on estimation results, is also studied. Results using flight data are compared with those obtained from a GPS-assisted Inertial Navigation System (INS), whereas for results using simulation data, the simulation itself provides true values. Conclusions are drawn for the performance of the algorithm based on simulation and flight data.

Ethogram of Immature Green Turtles: Behavioral Strategies for Somatic Growth in Large Marine Herbivores

Animals are assumed to obtain/conserve energy effectively to maximise their fitness, which manifests itself in a variety of behavioral strategies. For marine animals, however, these behavioral strategies are generally unknown due to the lack of high-resolution monitoring techniques in marine habitats. As large marine herbivores, immature green turtles do not need to allocate energy to reproduction but are at risk of shark predation, although it is a rare occurrence. They are therefore assumed to select/use feeding and resting sites that maximise their fitness in terms of somatic growth, while avoiding predation. We investigated fine-scale behavioral patterns (feeding, resting and other behaviors), microhabitat use and time spent on each behavior for eight immature green turtles using data loggers including: depth, global positioning system, head acceleration, speed and video sensors. Immature green turtles at Iriomote Island, Japan, spent an average of 4.8 h feeding on seagrass each day, with two peaks, between 5∶00 and 9∶00, and between 17∶00 and 20∶00. This feeding pattern appeared to be restricted by gut capacity, and thus maximised energy acquisition. Meanwhile, most of the remaining time was spent resting at locations close to feeding grounds, which allowed turtles to conserve energy spent travelling and reduced the duration of periods exposed to predation. These behavioral patterns and time allocations allow immature green turtles to effectively obtain/conserve energy for growth, thus maximising their fitness.

Scale Estimators in Target Mapping from an Aerial Vehicle

This paper describes the use of scale estimators in field trials for mapping ground targets by an aerial vehicle with onboard sensing. Targets are assumed to be static and of a known shape. The vehicle is a lightweight, tethered, airship with onboard global positioning system, magnetometer, 3-axis accelerometer and gyro sensors, and a camera. The dynamics, trajectory, and sensing noise characteristics, are assumed a priori unknown.The paper focus on the use of scale estimators to minimize the influence of the outlier measurements in the mean and covariance of the target positions observed as the vehicle moves. The results obtained show an interesting performance, better than that obtained with common maximum likelihood mean and covariance estimators, namely a smaller uncertainty is achieved.

Neural-network multiple models filter (NMM)-based position estimation system for autonomous vehicles

A highly accurate and reliable vehicle position estimation system is an important component of an autonomous driving system. In generally, a global positioning system (GPS) receiver is employed for the vehicle position estimation of autonomous vehicles. However, a stand-alone GPS does not always provide accurate and reliable information of the vehicle position due to frequent GPS blockages and multipath errors. In order to overcome these problems, a sensor fusion scheme that combines the data from the GPS receiver and several on-board sensors has been studied. In previous researches, a single model filter-based sensor fusion algorithm was used to integrate information from the GPS and on-board sensors. However, an estimate obtained from a single model is difficult to cover the various driving environments, including urban areas, off-road areas, and highways. Thus, a multiple models filter (MMF) has been introduced to address this limitation by adapting multiple models to a wide range of driving conditions. An adaptation of the multiple model is achieved through the use of the model probability. The MMF combines several vehicle models using the model probabilities, which indicate the suitability of the current driving condition. In this paper, we propose a vehicle position estimation algorithm for an autonomous vehicle that is based on a neural network (NN)-based MMF. The model probabilities are determined through the NN. The proposed position estimation system was evaluated through simulations and experiments. The experimental results show that the proposed position estimation algorithm is suitable for application in an autonomous driving system over a wide range of driving conditions.

Assessing Herbivore Foraging Behavior with GPS Collars in a Semiarid Grassland

Advances in global positioning system (GPS) technology have dramatically enhanced the ability to track and study distributions of free-ranging livestock. Understanding factors controlling the distribution of free-ranging livestock requires the ability to assess when and where they are foraging. For four years (2008–2011), we periodically collected GPS and activity sensor data together with direct observations of collared cattle grazing semiarid rangeland in eastern Colorado. From these data, we developed classification tree models that allowed us to discriminate between grazing and non-grazing activities. We evaluated: (1) which activity sensor measurements from the GPS collars were most valuable in predicting cattle foraging behavior, (2) the accuracy of binary (grazing, non-grazing) activity models vs. models with multiple activity categories (grazing, resting, traveling, mixed), and (3) the accuracy of models that are robust across years vs. models specific to a given year. A binary classification tree correctly removed 86.5% of the non-grazing locations, while correctly retaining 87.8% of the locations where the animal was grazing, for an overall misclassification rate of 12.9%. A classification tree that separated activity into four different categories yielded a greater misclassification rate of 16.0%. Distance travelled in a 5 minute interval and the proportion of the interval with the sensor indicating a head down position were the two most important variables predicting grazing activity. Fitting annual models of cattle foraging activity did not improve model accuracy compared to a single model based on all four years combined. This suggests that increased sample size was more valuable than accounting for interannual variation in foraging behavior associated with variation in forage production. Our models differ from previous assessments in semiarid rangeland of Israel and mesic pastures in the United States in terms of the value of different activity sensor measurements for identifying grazing activity, suggesting that the use of GPS collars to classify cattle grazing behavior will require calibrations specific to the environment and vegetation being studied.

듀얼 확장 칼만 필터를 이용한 쿼드로터 비행로봇 위치 정밀도 향상 알고리즘 개발

The fusion of the GPS (Global Positioning System) and DR (Dead Reckoning) is widely used for position and latitude estimation of vehicles such as a mobile robot, aerial vehicle and marine vehicle. Among the many types of aerial vehicles, grater focus is given on the quad-rotor and accuracy of the position information is becoming more important. In order to exactly estimate the position information, we propose the fusion method of GPS and Gyroscope sensor using the DEKF (Dual Extended Kalman Filter). The DEKF has an advantage of simultaneously estimating state value and a parameter of dynamical system. It can also be used even if state value is not available. In order to analyze the performance of DEKF, the computer simulation for estimating the position, the velocity and the angle in a circle trajectory of quad-rotor was done. As it can be seen from the simulation results using own proposed DEKF instead of EKF on own fusion method in the navigation of a quad-rotor gave better performance values.

Naturalistic Driving Observations of Manual and Visual–Manual Interactions with Navigation Systems and Mobile Phones While Driving

This paper discusses a naturalistic driving study on the use of mobile phones and navigation systems while driving. Manual interactions with these devices while driving can cause distraction from the driving task and reduce traffic safety. In this study 21 subjects were observed for 5 weeks. Their behavior was logged by four cameras, a Global Positioning System sensor, and a number of additional sensors. Results are presented on the durations and frequencies of manual interactions with the mobile phone and navigation system. Different manual subtasks with different levels of visual interaction are discussed, as well as how these interactions relate to driving speed. Results show that participants spent on average 1% of their driving time interacting with the navigation system and 4% of their driving time interacting with the mobile phone, excluding mobile phone conversations. For the mobile phone, 48% of the interactions took longer than 15 s; for the navigation system it was 40%. The average duration of a visual–manual subtask for the navigation system and the mobile phone was not significantly different when the task was performed while driving or while the vehicle was standing still. Exploratory analysis of vehicle speed suggests that drivers do not seem to adjust their speed while performing a visual–manual task. The results are discussed in relation to traffic safety and recommendations for future research.

Design and Implementation of Vehicle Self-Navigation System in Urban Intelligent Traffic

Compared with the single sensor measuring, the complementary measuring with the dual sensor can solve some key problems in the vehicle self-navigation system. Two sensors’ data must be fused by the federal state estimator. Based on the Singer model, the system equation of Dead Reckoning sensor is nonlinear and the system equation of Global Positioning System sensor is linear. A two-level estimator is designed and implemented in such a way that two local estimators process the linear and nonlinear systems respectively, and the main estimator fuses the data from two local estimators, so that the optimal global state variables can be estimated. The simulation results show that the two-level estimator of dual sensor can increase the vehicle’s self-navigation precision and can be applied in urban intelligent traffic.

Radiometric and Geometric Analysis of Hyperspectral Imagery Acquired from an Unmanned Aerial Vehicle

In the summer of 2010, an Unmanned Aerial Vehicle (UAV) hyperspectral calibration and characterization experiment of the Resonon PIKA II imaging spectrometer was conducted at the US Department of Energy’s Idaho National Laboratory (INL) UAV Research Park. The purpose of the experiment was to validate the radiometric calibration of the spectrometer and determine the georegistration accuracy achievable from the on-board global positioning system (GPS) and inertial navigation sensors (INS) under operational conditions. In order for low-cost hyperspectral systems to compete with larger systems flown on manned aircraft, they must be able to collect data suitable for quantitative scientific analysis. The results of the in-flight calibration experiment indicate an absolute average agreement of 96.3%, 93.7% and 85.7% for calibration tarps of 56%, 24%, and 2.5% reflectivity, respectively. The achieved planimetric accuracy was 4.6 m (based on RMSE) with a flying height of 344 m above ground level (AGL).

Observing Ocean Surface Waves with GPS-Tracked Buoys

Abstract Surface-following buoys are widely used to collect routine ocean wave measurements. While accelerometer and tilt sensors have been used for decades to measure the wave-induced buoy displacements, alternative global positioning system (GPS) sensor packages have been introduced recently that are generally smaller, less expensive, and do not require calibration. In this study, the capabilities of several GPS sensors are evaluated with field observations in wind-sea and swell conditions off the California coast. The GPS buoys used in this study include Datawell Directional Waverider and Mini Directional Waverider buoys equipped with a specialized GPS Doppler shift sensor, and a low-cost experimental drifter equipped with an “off the shelf” GPS receiver for absolute position tracking. Various GPS position receivers were attached to the Waverider buoys to evaluate their potential use in low-cost wave-resolving drifters. Intercomparisons between the Datawell GPS-based buoys, the experimental GPS drifter, and a conventional Datawell buoy with an accelerometer–tilt–compass sensor package, show good agreement in estimates of wave frequency and direction spectra. Despite the limited (several meters) absolute accuracy of the GPS position receivers, the horizontal wave orbital displacements are accurately resolved, even in benign (significant wave height less than 1 m) swell conditions. Vertical sea surface displacements were not well resolved by the GPS position receivers with built-in or small patch antennas, but accurately measured when an external precision antenna was attached to the drifter. Overall, the field tests show excellent agreement between Datawell buoys using GPS and motion-sensor packages, and demonstrate the feasibility of observing ocean surface waves with low-cost GPS-tracked drifters.

Coupling in-flight trajectory planning and flocking for multiple autonomous parafoils

Much effort has been invested during the past decades in design of parafoils for a wide range of payloads and in development of means for their guidance. Existing parafoils are capable of autonomous navigation using the global positioning system and other onboard sensors. The purpose of this study is to explore the advantages of coordination among multiple autonomous parafoils. Each parafoil is able to navigate to the target on its own by following a real-time-generated reference trajectory. A new method for trajectory generation is presented and behaviour-based rules are developed that control the relative motion of the descending parafoils. The set of simple rules results in an emergent behaviour known as flocking. The coupling between trajectory following and flocking is studied in a multiagent simulation. The simulation uses a realistic six-degrees-of-freedom model of a heavy cargo parafoil. The obtained results demonstrate the possibility of flocking behaviour for guided parafoils. The flocking rules ensure safe separation between the vehicles headed for the same target and allow the parafoils to follow a reference trajectory as a group.

Interacting Multiple Model Filter-Based Sensor Fusion of GPS With In-Vehicle Sensors for Real-Time Vehicle Positioning

Vehicle position estimation for intelligent vehicles requires not only highly accurate position information but reliable and continuous information provision as well. A low-cost Global Positioning System (GPS) receiver has widely been used for conventional automotive applications, but it does not guarantee accuracy, reliability, or continuity of position data when GPS errors occur. To mitigate GPS errors, numerous Bayesian filters based on sensor fusion algorithms have been studied. The estimation performance of Bayesian filters primarily relies on the choice of process model. For this reason, the change in vehicle dynamics with driving conditions should be addressed in the process model of the Bayesian filters. This paper presents a positioning algorithm based on an interacting multiple model (IMM) filter that integrates low-cost GPS and in-vehicle sensors to adapt the vehicle model to various driving conditions. The model set of the IMM filter is composed of a kinematic vehicle model and a dynamic vehicle model. The algorithm developed in this paper is verified via intensive simulation and evaluated through experimentation with a real-time embedded system. Experimental results show that the performance of the positioning system is accurate and reliable under a wide range of driving conditions.

지하수 모니터링을 위한 GeoSensor 기반의 이동식 보조관측망 프로토타입 시스템 및 사용자 인터페이스 구현

Although underground water resource has relatively less pollution rate compared with surface water, its recovery faces many difficulties due to poor management. Our country monitors underground water to manage it effectively through auxiliary observation network for underground water. In this paper, we suggest water-well auto measure system based on Geosensor for business efficiency increase of water-well management and realtime monitering. In this system is consist of user GUI(Graphic User Interface) composed with water-well information and movement sub-observation network prototype system composed with GPS(Global Positioning System) and wireless sensor node such as water temperature, water level, electrical conductivity. In this system is using the light of the sun for self-power, variety water-well information collected wireless sensor node was a wireless transmitting/receiving a using CDMA(Code Division Multiple Access) module. Also, for promote with user ease, user GUI express that water-well collected in GIS(Geographic Information System) map. For performance evaluation of the proposed system, we perform experiment using sensing information through designed sub-observation network. And we was proved superiority of the proposed system through qualitative evaluation with other paper.

Inferring Road Maps from Global Positioning System Traces

As a result of the availability of Global Positioning System (GPS) sensors in a variety of everyday devices, GPS trace data are becoming increasingly abundant. One potential use of this wealth of data is to infer and update the geometry and connectivity of road maps through the use of what are known as map generation or map inference algorithms. These algorithms offer a tremendous advantage when no existing road map data are present. Instead of the expense of a complete road survey, GPS trace data can be used to generate entirely new sections of the road map at a fraction of the cost. In cases of existing maps, road map inference may not only help to increase the accuracy of available road maps but may also help to detect new road construction and to make dynamic adaptions to road closures—useful features for in-car navigation with digital road maps. In past research, proposed algorithms had been evaluated qualitatively with little or no comparison with prior work. This lack of quantitative and comparative evaluation is addressed in this paper with the following contributions: (a) a comprehensive survey of the current literature on map generation; (b) a description of the first method for the automatic evaluation of generated maps; (c) a qualitative, quantitative, and comparative evaluation of three reference algorithms; and (d) an open source implementation of each of the three algorithms, with a 118-h trace data set and ground truth map for unrestricted use by the automatic map generation community.

Augmenting full colour-fused multi-band night vision imagery with synthetic imagery in real-time

We present the design and first field trial results of an all-day all-weather enhanced and synthetic-fused multi-band colour night vision surveillance and observation system. The system augments a fused and dynamic three-band natural-colour night vision image with synthetic 3D imagery in real-time. The night vision sensor suite consists of three cameras, sensitive in, respectively, the visual (400–700 nm), the near-infrared (NIR, 700–1000 nm) and the long-wave infrared (LWIR, 8–14 µm) bands of the electromagnetic spectrum. The optical axes of the three cameras are aligned. Image quality of the fused sensor signals is enhanced in real-time through dynamic noise reduction, super resolution and local adaptive contrast enhancement. The quality of the LWIR image is enhanced through scene-based non-uniformity correction. The visual and NIR signals are used to represent the fused multi-band night vision image in natural daytime colours, using the Colour-the-Night colour remapping technique. Colour remapping can also be deployed to enhance the visibility of thermal targets that are camouflaged in the visual and NIR range of the spectrum. The dynamic false-colour night-time images are augmented with corresponding synthetic 3D scene views, generated in real-time using a geometric 3D scene model in combination with position and orientation information supplied by the Global Positioning System and inertial sensors of the system. Initial field trials show that this system provides enhanced situational information in various low-visibility conditions.

A Real-Time Capable Software-Defined Receiver Using GPU for Adaptive Anti-Jam GPS Sensors

Due to their weak received signal power, Global Positioning System (GPS) signals are vulnerable to radio frequency interference. Adaptive beam and null steering of the gain pattern of a GPS antenna array can significantly increase the resistance of GPS sensors to signal interference and jamming. Since adaptive array processing requires intensive computational power, beamsteering GPS receivers were usually implemented using hardware such as field-programmable gate arrays (FPGAs). However, a software implementation using general-purpose processors is much more desirable because of its flexibility and cost effectiveness. This paper presents a GPS software-defined radio (SDR) with adaptive beamsteering capability for anti-jam applications. The GPS SDR design is based on an optimized desktop parallel processing architecture using a quad-core Central Processing Unit (CPU) coupled with a new generation Graphics Processing Unit (GPU) having massively parallel processors. This GPS SDR demonstrates sufficient computational capability to support a four-element antenna array and future GPS L5 signal processing in real time. After providing the details of our design and optimization schemes for future GPU-based GPS SDR developments, the jamming resistance of our GPS SDR under synthetic wideband jamming is presented. Since the GPS SDR uses commercial-off-the-shelf hardware and processors, it can be easily adopted in civil GPS applications requiring anti-jam capabilities.

A fuzzy logic map matching for wheelchair navigation

Global Positioning System (GPS) plays a central role in solving the location-based needs of wheelchair users and efficient route guidance. An essential process in wheelchair navigation is matching the position obtained from GPS or other sensors on a sidewalk network. This process of map matching in turn assists in making decisions under uncertainty. However, GPS-based wheelchair navigation systems have difficulties in tracking wheelchairs in urban areas due to poor satellite availability. To overcome this, a fuzzy logic-based algorithm is applied to effectively perform matching wheelchair movements on sidewalks. Fuzzy logic, based on fuzzy reasoning concepts, in many circumstances can take noisy and imprecise input to yield numerically accurate output. Experiments on a university campus validate it practicable.

Monitoring a steel building using GPS sensors

To assess the performance of a structure requires the measurement of global and relative displacements at critical points across the structure. They should be obtained in real time and in all weather condition. A Global Navigation Satellite System (GNSS) could satisfy the last two requirements. The American Global Position System (GPS) provides long term acquisitions with sampling rates sufficient to track the displacement of long period structures. The accuracy is of the order of sub-centimetres. The steel building which hosts the authors` laboratory is the reference case-study within this paper. First a comparison of data collected by GPS sensor units with data recorded by tri-axial accelerometers is carried out when dynamic vibrations are induced in the structure by movements of the internal bridge-crane. The elaborations from the GPS position readings are then compared with the results obtained by a Finite Element (FE) numerical simulation. The purposes are: i) to realize a refinement of the structural parameters which characterize the building and ii) to outline a suitable way for processing GPS data toward structural monitoring.