Interactive Planning and Sensing for Aircraft in Uncertain Environments with Spatiotemporally Evolving Threats

Autonomous aerial, terrestrial, and marine vehicles provide a platform for several applications including cargo transport, information gathering, surveillance, reconnaissance, and search-and-rescue. To enable such applications, two main technical problems are commonly addressed.On the one hand, the motion-planning problem addresses optimal motion to a destination: an application example is the delivery of a package in the shortest time with least fuel. Solutions to this problem often assume that all relevant information about the environment is available, possibly with some uncertainty. On the other hand, the information gathering problem addresses the maximization of some metric of information about the environment: application examples include such as surveillance and environmental monitoring. Solutions to the motion-planning problem in vehicular autonomy assume that information about the environment is available from three sources: (1) the vehicle’s own onboard sensors, (2) stationary sensor installations (e.g. ground radar stations), and (3) other information gathering vehicles, i.e., mobile sensors, especially with the recent emphasis on collaborative teams of autonomous vehicles with heterogeneous capabilities. Each source typically processes the raw sensor data via estimation algorithms. These estimates are then available to a decision making system such as a motion- planning algorithm. The motion-planner may use some or all of the estimates provided. There is an underlying assumption of “separation� between the motion-planning algorithm and the information about environment. This separation is common in linear feedback control systems, where estimation algorithms are designed independent of control laws, and control laws are designed with the assumption that the estimated state is the true state. In the case of motion-planning, there is no reason to believe that such a separation between the motion-planning algorithm and the sources of estimated environment information will lead to optimal motion plans, even if the motion planner and the estimators are themselves optimal. The goal of this dissertation is to investigate whether the removal of this separation, via interactive motion-planning and sensing, can significantly improve the optimality of motion- planning. The major contribution of this work is interactive planning and sensing. We consider the problem of planning the path of a vehicle, which we refer to as the actor, to traverse a threat field with minimum threat exposure. The threat field is an unknown, time- variant, and strictly positive scalar field defined on a compact 2D spatial domain – the actor’s workspace. The threat field is estimated by a network of mobile sensors that can measure the threat field pointwise. All measurements are noisy. The objective is to determine a path for the actor to reach a desired goal with minimum risk, which is a measure sensitive not only to the threat exposure itself, but also to the uncertainty therein. A novelty of this problem setup is that the actor can communicate with the sensor network and request that the sensors position themselves in a procedure we call sensor reconfiguration such that the actor’s risk is minimized. This work continues with a foundation in motion planning in time-varying fields where waiting is a control input. Waiting is examined in the context of finding an optimal path with considerations for the cost of exposure to a threat field, the cost of movement, and the cost of waiting. For example, an application where waiting may be beneficial in motion-planning is the delivery of a package where adverse weather may pose a risk to the safety of a UAV and its cargo. In such scenarios, an optimal plan may include “waiting until the storm passes.� Results on computational efficiency and optimality of considering waiting in path- planning algorithms are presented. In addition, the relationship of waiting in a time- varying field represented with varying levels of resolution, or multiresolution is studied. Interactive planning and sensing is further developed for the case of time-varying environments. This proposed extension allows for the evaluation of different mission windows, finite sensor network reconfiguration durations, finite planning durations, and varying number of available sensors. Finally, the proposed method considers the effect of waiting in the path planner under the interactive planning and sensing for time-varying fields framework. Future work considers various extensions of the proposed interactive planning and sensing framework including: generalizing the environment using Gaussian processes, sensor reconfiguration costs, multiresolution implementations, nonlinear parameters, decentralized sensor networks and an application to aerial payload delivery by parafoil

INS, GPS, and Photogrammetry Integration for Vector Gravimetry Estimation

Presented in Partial Fulfillment of the Requirement for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University.This work was supported by the U.S. Air Force under contract F19628-95-K- 0020 (Defense Mapping Agency funding) and by the National Imagery and Mapping Agency (formerly DMA) under contract NMA202-98-1-1110.Vector gravimetry using Inertial Navigation System (INS) in semi-kinematic mode has been successfully applied. The integration of INS with other sensors, Global Positioning System (GPS) or Gradiometer, for instance, has been under investigation for many years. This dissertation examines the effect of photogrammetric derived orientation on the INS sensor’s calibration and estimation of the gravity vector. The capability of such integration in estimating the INS biases and drifts is studied. The underlying principle, mathematical models, and error sources are presented and analyzed. The estimation process utilizes the measurements of the Litton LN-100 inertial system, Trimble 4000 SSI GPS dual frequency receiver, and metric frame camera. An optimal filtering technique is used to integrate both GPS and INS on the level of raw measurement for both systems. Introducing accurate and independent orientation parameters, e.g., the photogrammetric source in this study, is demonstrated to enable calibration of inertial gyros and bounding of their drift errors. This leads to improvement in the horizontal components of the gravity vector estimation. The estimability and improvement of the deflection of the vertical components are tested using flight test data over Oakland, California, and a set of photogrammetric images simulated along the flight trajectory. The error statistics of the orientation measurement are modeled on the basis of the variance-covariance matrix of a photogrammetric bundle adjustment of all photos. With just a few ground control points at the beginning of the trajectory, the orientation measurement errors along the trajectory are correlated significantly from epoch to epoch, thus reducing the information content of the external orientation estimates. The horizontal gravity component estimation is tested with respect to its sensitivity to the variance of the orientation measurement errors, to its auto-correlation in time, to the cross-correlation between angles, and to the amount of available ground control. Although photogrammetric measurements, if uncorrelated, control orientation errors as well as better than achievable with aircraft maneuvers, the inherent correlation with a very limited amount of ground control provides only a small improvement. On the basis of the simulation parameters, the gravity estimation error was reduced from 20 mgal (GPS/INS only) to about 9 mgal (best uncorrelated control) versus 17 mgal (correlated control)

Adaptive algorithms for identification of symmetric and positive definite matrices

Adaptive estimation and identification algorithms involving unknown symmetric and positive definite (SPD) matrix-valued parameters are ubiquitous in engineering applications. The problem of estimating the noise covariance matrices in estimation algorithms is considered first. An adaptive Kalman filter to estimate the noise covariance matrix of the noises entering a linear time invariant system is introduced first. The convergence of the estimates as well as the states is guaranteed with mild assumptions on the system. Conditions of estimability of the noise covariance matrix are discussed. The generalization of the adaptive Kalman fitler to the linear time varying case is introduced next. To maintain positive definiteness of the noise covariance estimates a differential geometric approach is adopted. The geometry of the manifold of SPD matrices is used to develop a Riemannian optimization based adaptive Kalman filter that ensure positive definiteness of the estimate. The convergence of the Riemannian optimization-based estimate and the adaptive Kalman filter is established under mild conditions of uniform observability and uniform controllability of the system. An adaptive control problem with an unknown SPD matrix is considered next. A novel projection scheme is introduced that ensures that the estimates of the unknown SPD matrix are SPD. Adaptive update laws for identifying the SPD matrix are also presented. The adaptive control laws are shown to globally stabilize systems in problems such as the adaptive angular velocity tracking, adaptive attitude control, and the adaptive trajectory tracking of robotic manipulators with parameter uncertainties within the generalized mass matrix. In general, such a method can be applied to estimation of symmetric matrices with eigenvalue constraints.Aerospace Engineerin

Orbital Effects in Spaceborne Synthetic Aperture Radar Interferometry

This book reviews and investigates orbit-related effects in synthetic aperture Radar interferometry (InSAR). The translation of orbit inaccuracies to error signals in the interferometric phase is concisely described; estimation and correction approaches are discussed and evaluated with special focus on network adjustment of redundantly estimated baseline errors. Moreover, the effect of relative motion of the orbit reference frame is addressed

High Resolution Vision-Based Servomechanism Using a Dynamic Target with Application to CNC Machines

This dissertation introduces a novel three dimensional vision-based servomechanism with application to real time position control for manufacturing equipment, such as Computer Numerical Control (CNC) machine tools. The proposed system directly observes the multi-dimensional position of a point on the moving tool relative to a fixed ground, thus bypassing the inaccurate kinematic model normally used to convert axis sensor-readings into an estimate of the tool position. A charge-coupled device (CCD camera) is used as the position transducer, which directly measures the current position error of the tool referenced to an absolute coordinate system. Due to the direct-sensing nature of the transducer no geometric error compensation is required. Two new signal processing algorithms, based on a recursive Newton-Raphson optimization routine, are developed to process the input data collected through digital imaging. The algorithms allow simultaneous high-precision position and orientation estimation from single readings. The desired displacement command of the tool in a planar environment is emulated, in one end of the kinematic chain, by an active element or active target pattern on a liquid-crystal display (LCD). On the other end of the kinematic chain the digital camera observes the active target and provides visual feedback information utilized for position control of the tool. Implementation is carried out on an XYθZ stage, which is position with high resolution. The introduction of the camera into the control loop yields a visual servo architecture; the dynamic problems and stability assessment of which are analyzed in depth for the case study of the single CAM- single image processing thread-configuration. Finally, two new command generation protocols are explained for full implementation of the proposed structure in real-time control applications. Command issuing resolutions do not depend upon the size of the smallest element of the grid/display being imaged, but can instead be determined in accordance with the sensor\u27s resolution

Performance Analysis of Bearings-only Tracking Problems for Maneuvering Target and Heterogeneous Sensor Applications

State estimation, i.e. determining the trajectory, of a maneuvering target from noisy measurements collected by a single or multiple passive sensors (e.g. passive sonar and radar) has wide civil and military applications, for example underwater surveillance, air defence, wireless communications, and self-protection of military vehicles. These passive sensors are listening to target emitted signals without emitting signals themselves which give them concealing properties. Tactical scenarios exists where the own position shall not be revealed, e.g. for tracking submarines with passive sonar or tracking an aerial target by means of electro-optic image sensors like infrared sensors. This estimation process is widely known as bearings-only tracking. On the one hand, a challenge is the high degree of nonlinearity in the estimation process caused by the nonlinear relation of angular measurements to the Cartesian state. On the other hand, passive sensors cannot provide direct target location measurements, so bearings-only tracking suffers from poor target trajectory estimation accuracy due to marginal observability from sensor measurements. In order to achieve observability, that means to be able to estimate the complete target state, multiple passive sensor measurements must be fused. The measurements can be recorded spatially distributed by multiple dislocated sensor platforms or temporally distributed by a single, moving sensor platform. Furthermore, an extended case of bearings-only tracking is given if heterogeneous measurements from targets emitting different types of signals, are involved. With this, observability can also be achieved on a single, not necessarily moving platform. In this work, a performance bound for complex motion models, i.e. piecewisely maneuvering targets with unknown maneuver change times, by means of bearings-only measurements from a single, moving sensor platform is derived and an efficient estimator is implemented and analyzed. Furthermore, an observability analysis is carried out for targets emitting acoustic and electromagnetic signals. Here, the different signal propagation velocities can be exploited to ensure observability on a single, not necessarily moving platform. Based on the theoretical performance and observability analyses a distributed fusion system has been realized by means of heterogeneous sensors, which shall detect an event and localize a threat. This is performed by a microphone array to detect sound waves emitted by the threat as well as a radar detector that detects electromagnetic emissions from the threat. Since multiple platforms are involved to provide increased observability and also redundancy against possible breakdowns, a WiFi mobile ad hoc network is used for communications. In order to keep up the network in a breakdown OLSR (optimized link state routing) routing approach is employed

Orbital Effects in Spaceborne Synthetic Aperture Radar Interferometry

This book reviews and investigates orbit-related effects in synthetic aperture Radar interferometry (InSAR). The translation of orbit inaccuracies to error signals in the interferometric phase is concisely described; estimation and correction approaches are discussed and evaluated with special focus on network adjustment of redundantly estimated baseline errors. Moreover, the effect of relative motion of the orbit reference frame is addressed

Shack-Hartmann and Interferometric Hybrid Wavefront Sensor

This document reports results of wave-optics simulations used to test the performance of a hybrid wavefront sensor designed to combine the self-referencing interferometer and Shack-Hartmann wavefront sensors in an optimal way. Optimal hybrid-wavefront sensor design required a thorough analysis of the noise characteristics of each wavefront sensor to produce noise models that assist in the design of an optimal phase-estimation algorithm. Feasible architectures and algorithms for combining wavefront sensors were chosen, and the noise models of the individual wavefront sensors were combined to form a model for the noise-induced error of the resulting hybrid sensor. The hybrid wavefront sensor and phase-estimation algorithm developed through this work showed improvement over a comparable stand-alone self-referencing interferometer and Shack-Hartmann wavefront sensor in open-loop wave-optics simulations

Preface

DAMSS-2018 is the jubilee 10th international workshop on data analysis methods for software systems, organized in Druskininkai, Lithuania, at the end of the year. The same place and the same time every year. Ten years passed from the first workshop. History of the workshop starts from 2009 with 16 presentations. The idea of such workshop came up at the Institute of Mathematics and Informatics. Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea. This idea got approval both in the Lithuanian research community and abroad. The number of this year presentations is 81. The number of registered participants is 113 from 13 countries. In 2010, the Institute of Mathematics and Informatics became a member of Vilnius University, the largest university of Lithuania. In 2017, the institute changes its name into the Institute of Data Science and Digital Technologies. This name reflects recent activities of the institute. The renewed institute has eight research groups: Cognitive Computing, Image and Signal Analysis, Cyber-Social Systems Engineering, Statistics and Probability, Global Optimization, Intelligent Technologies, Education Systems, Blockchain Technologies. The main goal of the workshop is to introduce the research undertaken at Lithuanian and foreign universities in the fields of data science and software engineering. Annual organization of the workshop allows the fast interchanging of new ideas among the research community. Even 11 companies supported the workshop this year. This means that the topics of the workshop are actual for business, too. Topics of the workshop cover big data, bioinformatics, data science, blockchain technologies, deep learning, digital technologies, high-performance computing, visualization methods for multidimensional data, machine learning, medical informatics, ontological engineering, optimization in data science, business rules, and software engineering. Seeking to facilitate relations between science and business, a special session and panel discussion is organized this year about topical business problems that may be solved together with the research community. This book gives an overview of all presentations of DAMSS-2018.DAMSS-2018 is the jubilee 10th international workshop on data analysis methods for software systems, organized in Druskininkai, Lithuania, at the end of the year. The same place and the same time every year. Ten years passed from the first workshop. History of the workshop starts from 2009 with 16 presentations. The idea of such workshop came up at the Institute of Mathematics and Informatics. Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea. This idea got approval both in the Lithuanian research community and abroad. The number of this year presentations is 81. The number of registered participants is 113 from 13 countries. In 2010, the Institute of Mathematics and Informatics became a member of Vilnius University, the largest university of Lithuania. In 2017, the institute changes its name into the Institute of Data Science and Digital Technologies. This name reflects recent activities of the institute. The renewed institute has eight research groups: Cognitive Computing, Image and Signal Analysis, Cyber-Social Systems Engineering, Statistics and Probability, Global Optimization, Intelligent Technologies, Education Systems, Blockchain Technologies. The main goal of the workshop is to introduce the research undertaken at Lithuanian and foreign universities in the fields of data science and software engineering. Annual organization of the workshop allows the fast interchanging of new ideas among the research community. Even 11 companies supported the workshop this year. This means that the topics of the workshop are actual for business, too. Topics of the workshop cover big data, bioinformatics, data science, blockchain technologies, deep learning, digital technologies, high-performance computing, visualization methods for multidimensional data, machine learning, medical informatics, ontological engineering, optimization in data science, business rules, and software engineering. Seeking to facilitate relations between science and business, a special session and panel discussion is organized this year about topical business problems that may be solved together with the research community. This book gives an overview of all presentations of DAMSS-2018