Application Of Kalman Filter With Time Correlated Measurement Errors In Subsurface Contaminant Transport Modeling

Contaminant transport modeling of a conservative solute in the subsurface is investigated by applying a Kalman filter (KF) with time correlated measurement errors. The usual method or assumption often employed is white Gaussian errors, but timecorrelated measurement errors were used instead for this research, since some hydrological observation data exhibit time correlated error characteristics. An observation data was generated from a two dimensiona

Satellite Formation-Flying and Rendezvous

GNSS has come to play an increasingly important role in satellite formation-flying and rendezvous applications. In the last decades, the use of GNSS measurements has provided the primary technique for determining the relative position of cooperative co-orbiting satellites in low Earth orbit

Višerazinska procjena faznih i kodnih pomaka satelitskog signala

Precise point positioning with satellite navigation signals requires knowledge of satellite code and phase biases. In this paper, a new multi-stage method is proposed for estimating of these biases using measurements from a geodetic network. Themethod first subtracts all available a priori knowledge on orbits, satellite clocks andmultipath from the measurements to reduce their dynamics. Secondly, satellite phase biases, ionospheric delays, carrier phase integer ambiguities and the geometry combining all non-dispersive parameters are jointly estimated in a Kalman filter. Finally, the a posteriori geometry estimates are refined in a second Kalman filter for the computation of orbital errors, code biases and tropospheric delays. As the first Kalman filter introduces time correlation, a generalized Kalman filter for colored measurement noise is applied in the second stage. The proposed algorithm is applied to dual frequency GPS measurements from a local geodetic network in Germany. A remarkable bias stability with variations of less than 3 cm over 4 hours is observed.Precizno odre.ivanje položaja uporabom satelitske navigacije zahtjeva poznavanje satelitskog koda te fazna mjerenja. U ovom radu predložena je nova metoda za procjenu faznih pomaka signala uporabom rezultata mjerenja iz geodetske mreže. U prvom koraku iz mjerenja se izuzimaju poznati podaci o orbitama, satelitskim satovima i višestrukim putevima, kako bi se smanjila njihova dinamika. U drugom se koraku uporabom Kalmanovog filtra procjenjuju fazni pomaci, ionosferska kašnjenja, neodre.enost broja valnih duljina nosioca i geometrija koja uključuje sve nedisperzivne parametre. Konačno, odre.uje se korigirana geometrija u drugom Kalmanovom filtru radi proračuna orbitalnih pogrešaka, pogrešaka koda i troposferskog kašnjenja. S obzirom na to da prvi Kalmanov filtar unosi vremensku korelaciju, opći Kalmanov filtar primjenjuje se u drugom koraku. Predloženi algoritam primijenjen je u dvofrekvencijskim GPS-mjerenjima u lokalnoj geodetskoj mreži u Njemačkoj. Postignuta je visoka stabilnost rezultata uz varijacije manje od 3 cm tijekom 4 sata

Object Tracking in Distributed Video Networks Using Multi-Dimentional Signatures

From being an expensive toy in the hands of governmental agencies, computers have evolved a long way from the huge vacuum tube-based machines to today\u27s small but more than thousand times powerful personal computers. Computers have long been investigated as the foundation for an artificial vision system. The computer vision discipline has seen a rapid development over the past few decades from rudimentary motion detection systems to complex modekbased object motion analyzing algorithms. Our work is one such improvement over previous algorithms developed for the purpose of object motion analysis in video feeds. Our work is based on the principle of multi-dimensional object signatures. Object signatures are constructed from individual attributes extracted through video processing. While past work has proceeded on similar lines, the lack of a comprehensive object definition model severely restricts the application of such algorithms to controlled situations. In conditions with varying external factors, such algorithms perform less efficiently due to inherent assumptions of constancy of attribute values. Our approach assumes a variable environment where the attribute values recorded of an object are deemed prone to variability. The variations in the accuracy in object attribute values has been addressed by incorporating weights for each attribute that vary according to local conditions at a sensor location. This ensures that attribute values with higher accuracy can be accorded more credibility in the object matching process. Variations in attribute values (such as surface color of the object) were also addressed by means of applying error corrections such as shadow elimination from the detected object profile. Experiments were conducted to verify our hypothesis. The results established the validity of our approach as higher matching accuracy was obtained with our multi-dimensional approach than with a single-attribute based comparison

Synthesis of Satellite Microwave Observations for Monitoring Global Land-Atmosphere CO2 Exchange

This dissertation describes the estimation, error quantification, and incorporation of land surface information from microwave satellite remote sensing for modeling global ecosystem land-atmosphere net CO2 exchange. Retrieval algorithms were developed for estimating soil moisture, surface water, surface temperature, and vegetation phenology from microwave imagery timeseries. Soil moisture retrievals were merged with model-based soil moisture estimates and incorporated into a light-use efficiency model for vegetation productivity coupled to a soil decomposition model. Results, including state and uncertainty estimates, were evaluated with a global eddy covariance flux tower network and other independent global model- and remote-sensing based products

Separablity of deformations and measurement noises of GPS time series with modified Kalman filter for landslide monitoring in real-time

The separation of the deformations and measurement noise of GPS coordinate time series and accuracy improvement of GPS real-time coordinates are major aspects of the thesis. In order to reduce the influence of the colored noise in the GPS position time series, three different methods have been compared: the Finite Impulse Response (FIR) filter, the Kalman filter model, and the sequential algorithm. Among these three methods, the Kalman filter is investigated in detail. The GPS real-time series contains the colored noise, yet the Kalman filter model requires white noise. The state vector can be augmented by appending to the state vector components of the shaping filter which can describe the long term movement of the colored noise. Thus the deformation analysis based on the Kalman filter model with a shaping filter technique, has been applied in the different movement trends of GPS real-time series. From the results, the Kalman filter model with a shaping filter can be widely used to process the GPS short baseline time series in real-time. The precise position coordinate can be obtained and the deformation epoch can be detected in time and with high reliability. It can be applied in the early warning system of the natural hazards. The detection of a deformation with less time delay and the improvement of reliability of detecting deformation epoch is another key issue of the investigation. The proposed model makes use of the statistical criterion (MDL criterion) comparison instead of the hypothesis test. Considering the affection of colored noise in the GPS time series the multiple Kalman filters model was augmented by shaping filters which describe the long-term movement of the colored noise. By the GPS experiments, it has been verified that the proposed models have the ability to better capture the deformation epoch and to improve the reliability of detecting the deformation epoch. The proposed models can be used to detect stepwise changes of a variety of fields in real-time or near real-time.Schwerpunkte dieser Arbeit sind die Trennung von tatsächlicher Bewegung und Messrauschen in GPS-Koordinatenzeitreihen und die Genauigkeitssteigerung von Echtzeit-GPS-Koordinaten. Zur Verringerung des Einflusses von farbigem Rauschen bei Zeitreihen von GPS-Positionen wurden drei verschiedene Verfahren verglichen: FIR-Filter (Finite Impulse Response),Kalman-Filter-Modell und Sequentielle Ausgleichung. Von diesen drei Verfahren wird das Kalman-Filter genauer untersucht. In Echtzeit-GPS-Datenreihen ist farbiges Rauschen enthalten, das Kalman-Filter hingegen erfordert weißes Rauschen. Die Zustandsschätzung erfolgt durch die Erweiterung des Zustandsvektors um die shaping-Filter-Komponenten, die den langfristigen Einfluss des farbigen Rauschprozesses beschreiben. Dementsprechend wurde die Bewegungsanalyse durch ein Kalman-Filter-Modell mit shaping-Filter-Verfahren auf verschiedene Rauschprozesse von Echtzeit-GPS-Zeitreihen angewandt. Das Ergebnis ist, dass ein Kalman-Filter mit shaping-Filter kann häufig zur Echtzeitauswertung von Zeitreihen kurzer GPS-Basislinien genutzt werden. Die genauen Positionskoordinaten lassen sich bestimmen, und, eine Bewegungsepoche kann rechtzeitig und mit einer hohen Zuverlässigkeit bestimmt werden. Ein Einsatz in Frühwarnsystemen vor Naturgefahren ist möglich. Die Erkennung von Bewegung mit geringer Zeitverzögerung und die Steigerung der Detektionszu-verlässigkeit von Bewegungsepochen sind weitere Untersuchungsschwerpunkte. Der vorgeschlagene Ansatz nutzt statt eines Hypothesentests den Vergleich eines statistischen Kriteriums (Minimum Desciption Length). In Anbetracht des farbigen Rauschens, das in GPS-Zeitreihen enthalten ist, wurde das multiple Kalman-Filter um shaping-Filter erweitert, die den langfristigen Einfluss des farbigen Rauschens beschreiben. Durch GPS- Experiment konnte nachgewiesen werden, dass die vorgeschlagenen Modelle eine verbesserte Deformationserkennung und eine Steigerung der Zuverlässigkeit bezüglich der Deformationsepochendetektion ermöglichen. Diese erlauben die Erkennung stufenförmiger Änderungen bei vielfältigen Anwendungen und zur Vorhersage einiger Naturkatastrophenereignisse in Echtzeit beziehungsweise Nahezu-Echtzeit

Cooperative Relative Positioning for Vehicular Environments

Fahrerassistenzsysteme sind ein wesentlicher Baustein zur Steigerung der Sicherheit im Straßenverkehr. Vor allem sicherheitsrelevante Applikationen benötigen eine genaue Information über den Ort und der Geschwindigkeit der Fahrzeuge in der unmittelbaren Umgebung, um mögliche Gefahrensituationen vorherzusehen, den Fahrer zu warnen oder eigenständig einzugreifen. Repräsentative Beispiele für Assistenzsysteme, die auf eine genaue, kontinuierliche und zuverlässige Relativpositionierung anderer Verkehrsteilnehmer angewiesen sind, sind Notbremsassitenten, Spurwechselassitenten und Abstandsregeltempomate. Moderne Lösungsansätze benutzen Umfeldsensorik wie zum Beispiel Radar, Laser Scanner oder Kameras, um die Position benachbarter Fahrzeuge zu schätzen. Dieser Sensorsysteme gemeinsame Nachteile sind deren limitierte Erfassungsreichweite und die Notwendigkeit einer direkten und nicht blockierten Sichtlinie zum Nachbarfahrzeug. Kooperative Lösungen basierend auf einer Fahrzeug-zu-Fahrzeug Kommunikation können die eigene Wahrnehmungsreichweite erhöhen, in dem Positionsinformationen zwischen den Verkehrsteilnehmern ausgetauscht werden. In dieser Dissertation soll die Möglichkeit der kooperativen Relativpositionierung von Straßenfahrzeugen mittels Fahrzeug-zu-Fahrzeug Kommunikation auf ihre Genauigkeit, Kontinuität und Robustheit untersucht werden. Anstatt die in jedem Fahrzeug unabhängig ermittelte Position zu übertragen, werden in einem neuartigem Ansatz GNSS-Rohdaten, wie Pseudoranges und Doppler-Messungen, ausgetauscht. Dies hat den Vorteil, dass sich korrelierte Fehler in beiden Fahrzeugen potentiell herauskürzen. Dies wird in dieser Dissertation mathematisch untersucht, simulativ modelliert und experimentell verifiziert. Um die Zuverlässigkeit und Kontinuität auch in "gestörten" Umgebungen zu erhöhen, werden in einem Bayesischen Filter die GNSS-Rohdaten mit Inertialsensormessungen aus zwei Fahrzeugen fusioniert. Die Validierung des Sensorfusionsansatzes wurde im Rahmen dieser Dissertation in einem Verkehrs- sowie in einem GNSS-Simulator durchgeführt. Zur experimentellen Untersuchung wurden zwei Testfahrzeuge mit den verschiedenen Sensoren ausgestattet und Messungen in diversen Umgebungen gefahren. In dieser Arbeit wird gezeigt, dass auf Autobahnen, die Relativposition eines anderen Fahrzeugs mit einer Genauigkeit von unter einem Meter kontinuierlich geschätzt werden kann. Eine hohe Zuverlässigkeit in der longitudinalen und lateralen Richtung können erzielt werden und das System erweist 90% der Zeit eine Unsicherheit unter 2.5m. In ländlichen Umgebungen wächst die Unsicherheit in der relativen Position. Mit Hilfe der on-board Sensoren können Fehler bei der Fahrt durch Wälder und Dörfer korrekt gestützt werden. In städtischen Umgebungen werden die Limitierungen des Systems deutlich. Durch die erschwerte Schätzung der Fahrtrichtung des Ego-Fahrzeugs ist vor Allem die longitudinale Komponente der Relativen Position in städtischen Umgebungen stark verfälscht.Advanced driver assistance systems play an important role in increasing the safety on today's roads. The knowledge about the other vehicles' positions is a fundamental prerequisite for numerous safety critical applications, making it possible to foresee critical situations, warn the driver or autonomously intervene. Forward collision avoidance systems, lane change assistants or adaptive cruise control are examples of safety relevant applications that require an accurate, continuous and reliable relative position of surrounding vehicles. Currently, the positions of surrounding vehicles is estimated by measuring the distance with e.g. radar, laser scanners or camera systems. However, all these techniques have limitations in their perception range, as all of them can only detect objects in their line-of-sight. The limited perception range of today's vehicles can be extended in future by using cooperative approaches based on Vehicle-to-Vehicle (V2V) communication. In this thesis, the capabilities of cooperative relative positioning for vehicles will be assessed in terms of its accuracy, continuity and reliability. A novel approach where Global Navigation Satellite System (GNSS) raw data is exchanged between the vehicles is presented. Vehicles use GNSS pseudorange and Doppler measurements from surrounding vehicles to estimate the relative positioning vector in a cooperative way. In this thesis, this approach is shown to outperform the absolute position subtraction as it is able to effectively cancel out common errors to both GNSS receivers. This is modeled theoretically and demonstrated empirically using simulated signals from a GNSS constellation simulator. In order to cope with GNSS outages and to have a sufficiently good relative position estimate even in strong multipath environments, a sensor fusion approach is proposed. In addition to the GNSS raw data, inertial measurements from speedometers, accelerometers and turn rate sensors from each vehicle are exchanged over V2V communication links. A Bayesian approach is applied to consider the uncertainties inherently to each of the information sources. In a dynamic Bayesian network, the temporal relationship of the relative position estimate is predicted by using relative vehicle movement models. Also real world measurements in highway, rural and urban scenarios are performed in the scope of this work to demonstrate the performance of the cooperative relative positioning approach based on sensor fusion. The results show that the relative position of another vehicle towards the ego vehicle can be estimated with sub-meter accuracy in highway scenarios. Here, good reliability and 90% availability with an uncertainty of less than 2.5m is achieved. In rural environments, drives through forests and towns are correctly bridged with the support of on-board sensors. In an urban environment, the difficult estimation of the ego vehicle heading has a mayor impact in the relative position estimate, yielding large errors in its longitudinal component