Meta-optimization of the Extended Kalman filter's parameters through the use of the Bias-Variance Equilibrium Point criterion

The extraction of information on land cover classes using unsupervised methods has always been of relevance to the remote sensing community. In this paper, a novel criterion is proposed, which extracts the inherent information in an unsupervised fashion from a time series. The criterion is used to fit a parametric model to a time series, derive the corresponding covariance matrices of the parameters for the model, and estimate the additive noise on the time series. The proposed criterion uses both spatial and temporal information when estimating the covariance matrices and can be extended to incorporate spectral information. The algorithm used to estimate the parameters for the model is the extended Kalman filter (EKF). An unsupervised search algorithm, specifically designed for this criterion, is proposed in conjunction with the criterion that is used to rapidly and efficiently estimate the variables. The search algorithm attempts to satisfy the criterion by employing density adaptation to the current candidate system. The application in this paper is the use of an EKF to model Moderate Resolution Imaging Spectroradiometer time series with a triply modulated cosine function as the underlying model. The results show that the criterion improved the fit of the triply modulated cosine function by an order of magnitude on the time series over all seven spectral bands when compared with the other methods. The state space variables derived from the EKF are then used for both land cover classification and land cover change detection. The method was evaluated in the Gauteng province of South Africa where it was found to significantly improve on land cover classification and change detection accuracies when compared with other methods.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=36hb201

Multivariate data assimilation in snow modelling at Alpine sites

The knowledge of snowpack dynamics is of critical importance to several real-time applications such as agricultural production, water resource management, flood prevention, hydropower generation, especially in mountain basins. Snowpack state can be estimated by models or from observations, even though both these sources of information are affected by several errors

Investigation related to multispectral imaging systems

A summary of technical progress made during a five year research program directed toward the development of operational information systems based on multispectral sensing and the use of these systems in earth-resource survey applications is presented. Efforts were undertaken during this program to: (1) improve the basic understanding of the many facets of multispectral remote sensing, (2) develop methods for improving the accuracy of information generated by remote sensing systems, (3) improve the efficiency of data processing and information extraction techniques to enhance the cost-effectiveness of remote sensing systems, (4) investigate additional problems having potential remote sensing solutions, and (5) apply the existing and developing technology for specific users and document and transfer that technology to the remote sensing community

Performance of Sensor Fusion for Vehicular Applications

Sensor fusion is a key system in Advanced Driver Assistance Systems, ADAS. The perfor-mance of the sensor fusion depends on many factors such as the sensors used, the kinematicmodel used in the Extended Kalman Filter, EKF, the motion of the vehicles, the type ofroad, the density of vehicles, and the gating methods. The interactions between parametersand the extent to which individual parameters contribute to the overall accuracy of a sensorfusion system can be difficult to assess.In this study, a full-factorial experimental evaluation of a sensor fusion system basedon a real vehicle was performed. The experimental results for different driving scenariosand parameters are discussed and the factors that make the most impact are identified.The performance of sensor fusion depends on many factors such as the sensors used, thekinematic model used in the Extended Kalman Filter (EKF) motion of the vehicles, type ofroad, density of vehicles, and gating methods.This study identified that the distance between the vehicles has the largest impact on theestimation error because the vision sensor performs poorly with increased distance. In addi-tion, it was identified that the kinematic models had no significant impact on the estimation.Last but not least, the ellipsoid gates performed better than rectangular gates.In addition, we propose a new gating algorithm called an angular gate. This algorithmis based on the observation that the data for each target lies in the direction of that target.Therefore, the angle and the range can be used for setting up a two-level gating approachthat is both more intuitive and computationally faster than ellipsoid gates. The angulargates can achieve a speedup factor of up to 2.27 compared to ellipsoid gates.Furthermore, we provide time complexity analysis of angular gates, ellipsoid gates, andrectangular gates demonstrating the theoretical reasons why angular gates perform better.Last, we evaluated the performance of the Munkres algorithm using a full factorial designand identified that narrower gates can speedup the running time of the Munkres algorithmand, surprisingly, even improve the RMSE in some cases.The low target maneuvering index of vehicular systems was identified as the reason whythe kinematic models do not have an impact on the estimation. This finding supports the useof simpler and computationally inexpensive filters instead of complex Interacting MultipleModel filters. The angular gates also improve the computational efficiency of the overallsensor fusion system making them suitable for vehicular application as well as for embeddedsystems and robotics

Doctor of Philosophy

dissertationA safe and secure transportation system is critical to providing protection to those who employ it. Safety is being increasingly demanded within the transportation system and transportation facilities and services will need to adapt to change to provide it. This dissertation provides innovate methodologies to identify current shortcomings and provide theoretic frameworks for enhancing the safety and security of the transportation network. This dissertation is designed to provide multilevel enhanced safety and security within the transportation network by providing methodologies to identify, monitor, and control major hazards associated within the transportation network. The risks specifically addressed are: (1) enhancing nuclear materials sensor networks to better deter and interdict smugglers, (2) use game theory as an interdiction model to design better sensor networks and forensically track smugglers, (3) incorporate safety into regional transportation planning to provide decision-makers a basis for choosing safety design alternatives, and (4) use a simplified car-following model that can incorporate errors to predict situational-dependent safety effects of distracted driving in an ITS infrastructure to deploy live-saving countermeasures

Autonomous and Resilient Management of All-Source Sensors for Navigation Assurance

All-source navigation has become increasingly relevant over the past decade with the development of viable alternative sensor technologies. However, as the number and type of sensors informing a system increases, so does the probability of corrupting the system with sensor modeling errors, signal interference, and undetected faults. Though the latter of these has been extensively researched, the majority of existing approaches have constrained faults to biases, and designed algorithms centered around the assumption of simultaneously redundant, synchronous sensors with valid measurement models, none of which are guaranteed for all-source systems. This research aims to provide all-source multi-sensor resiliency, assurance, and integrity through an autonomous sensor management framework. The proposed framework dynamically places each sensor in an all-source system into one of four modes: monitoring, validation, calibration, and remodeling. Each mode contains specific and novel realtime processes that affect how a navigation system responds to sensor measurements. The monitoring mode is driven by a novel sensor-agnostic fault detection, exclusion, and integrity monitoring method that minimizes the assumptions on the fault type, all-source sensor composition, and the number of faulty sensors. The validation mode provides a novel method for the online validation of sensors which have questionable sensor models, in a fault-agnostic and sensor-agnostic manner, and without compromising the ongoing navigation solution in the process. The remaining two modes, calibration and remodeling, generalize and integrate online calibration and model identification processes to provide autonomous and dynamic estimation of candidate model functions and their parameters, which when paired with the monitoring and validation processes, directly enable resilient, self-correcting, plug-and-play open architecture navigation systems

Advancing Urban Flood Resilience With Smart Water Infrastructure

Advances in wireless communications and low-power electronics are enabling a new generation of smart water systems that will employ real-time sensing and control to solve our most pressing water challenges. In a future characterized by these systems, networks of sensors will detect and communicate flood events at the neighborhood scale to improve disaster response. Meanwhile, wirelessly-controlled valves and pumps will coordinate reservoir releases to halt combined sewer overflows and restore water quality in urban streams. While these technologies promise to transform the field of water resources engineering, considerable knowledge gaps remain with regards to how smart water systems should be designed and operated. This dissertation presents foundational work towards building the smart water systems of the future, with a particular focus on applications to urban flooding. First, I introduce a first-of-its-kind embedded platform for real-time sensing and control of stormwater systems that will enable emergency managers to detect and respond to urban flood events in real-time. Next, I introduce new methods for hydrologic data assimilation that will enable real-time geolocation of floods and water quality hazards. Finally, I present theoretical contributions to the problem of controller placement in hydraulic networks that will help guide the design of future decentralized flood control systems. Taken together, these contributions pave the way for adaptive stormwater infrastructure that will mitigate the impacts of urban flooding through real-time response.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163144/1/mdbartos_1.pd

MMAE Detection of Interference/Jamming and Spoofing in a DPGS-Aided Inertial System

Previous research at AFIT has resulted in the development of a DGPS-aided INS-based precision landing system (PLS) capable of meeting the FAA precision requirements for instrument landings. The susceptibility of DGPS transmissions to interference/jamming and spoofing must be addressed before DGPS may be used in such a safety-of-flight critical role. This thesis applies multiple model adaptive estimation (MMAE) techniques to the problem of detecting and identifying interference/jamming and spoofing failures in the DGPS signal. Such an MMAE is composed of a bank of parallel filters, each hypothesizing a different failure status, along with an evaluation of the current probability of each hypothesis being correct, to form a probability-weighted average output. Performance for a representative selection of navigation component cases is examined. For interference/jamming failures represented as increased measurement noise variance, results show that, because of the good FDI performance using MMAE, the blended navigation performance is essentially that of a single extended Kalman filter artificially informed of the actual interference noise variance. Standard MMAE is completely unable to detect spoofing failures (modelled as a bias or ramp offset signal directly added to the measurement). This thesis shows the development of a moving-bank pseudo-residual MMAE (PRMMAE) to detect and identify spoofing failures. Using the PRMMAE algorithm, the resulting navigation performance is equivalent to that of an extended Kalman filter operating in a no-fail environment