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

Moving object localization using frequency measurements

This research investigates the ability of locating a moving object using the Doppler shifts of a carrier frequency signal sent or re ected by the object and observed by several fixed or moving sensors spatially distributed in the 2-D or 3-D space. The idea was previously studied and several solutions are proposed based on exhaustive grid search or numerical polynomial optimization. We shall formulate the problem as a constrained optimization and propose two efficient solutions. The first is by using linear optimization method to reach a closed-form solution and the second is through semi-definite relaxation technique to achieve a noise resilient estimate. The solutions are derived first for the single-time measurement and then developed to multipletime observations collected during a short time interval in which the object motion is linear. Several scenarios are considered including 2-D and 3-D localization geometry, the sensors are fixed or moving along nonlinear trajectory with random speed, the presence of errors in the carrier frequency and the sensor positions, and the noncooperative object scenario where the frequency of the carrier signal is completely not known. Analysis validates the algebraic closed-form solution in reaching the Cramer- Rao Lower Bound accuracy under Gaussian noise within the small error region. The simulations show good performance for the proposed algorithms and support the theoretical analysis.Includes bibliographical references

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing

Dynamic modeling and characterization of magnetic hybrid films of polyvinyl butyral/iron oxide nanoparticles (PVB/Fe₂O₃) devoted to microactuators.

This thesis was accomplished in a dual-degree modality between the consolidated group of Synthesis and Characterization of Materials ꟷFacultad de Ingeniería Mecánica y Eléctrica (FIME), Universidad Autónoma de Nuevo León (UANL), México, and the research group of Methodologies for Automatic Control and for Design of Mechatronic Systems (MACS), department of Automatic Control and Micro-Mechatronic Systems ꟷ FEMTO-ST institute, Université Bourgogne Franche-Comté (UBFC), France

Aeronautical Engineering: A continuing bibliography with indexes (supplement 177)

This bibliography lists 469 reports, articles and other documents introduced into the NASA scientific and technical information system in July 1984

Synthesis and Assessment of Sustainable Fuels for Transportation and Space Exploration

As global energy sources transition towards renewable energy, the demand for sustainable fuels has never been greater. The sheer scale of this transition will require numerous solutions to accommodate for the diverse and complex situations worldwide. This dissertation will discuss 3 studies: the utilization of CO2 waste gas to produce fuels sustainably, characterizing biofuels for efficient use in automobiles, and developing a solid, emissonless fuel intended for spaceflight but also applicable on Earth. The hydrogenation of CO2 into value-added molecules could reduce greenhouse gas emissions if waste stream CO2 were captured for conversion. We found that atomic vacancies induced in defect-laden hexagonal boron nitride (dh-BN) can activate the CO2 molecule for hydrogenation. Subsequent hydrogenation to formic acid (HCOOH) and methanol (CH3OH) occur through vacancy-facilitated co-adsorption of hydrogen and CO2. Boron and nitrogen are abundant elements, making h-BN an attractive catalyst in the synthesis of value-added molecules, facilitating efforts to reduce GHG emissions. Biofuels could be vital in a sustainable fuel future. However, their implementation into existing engines requires an understanding of their interactions with engine components at temperature. The formation of carbon deposits on hot metal components can reduce engine performance. Using a novel test rig and gasoline and diesel analog compounds, the degree of fuel degradation to form carbon can be measured on various metal surfaces. Thus, we can screen for low soot-forming biofuels as promising candidates surface on the market. Historically, innovations in space exploration have led to immensely beneficial applications on Earth. Currently, various limitations of power sources hinder the capacity for regular and frequent space exploration. The ability to harvest heat for electrical power would reduce the cost of long-distance and long-duration missions. Employing a regulated, self-propagating, exothermic chemical reaction, we have devised a slow-burning reactant system capable of generating heat at a harvestable rate

Sequential Monte Carlo Methods With Applications To Communication Channels

Estimating the state of a system from noisy measurements is a problem which arises in a variety of scientific and industrial areas which include signal processing, communications, statistics and econometrics. Recursive filtering is one way to achieve this by incorporating noisy observations as they become available with prior knowledge of the system model. Bayesian methods provide a general framework for dynamic state estimation problems. The central idea behind this recursive Bayesian estimation is computing the probability density function of the state vector of the system conditioned on the measurements. However, the optimal solution to this problem is often intractable because it requires high-dimensional integration. Although we can use the Kalman lter in the case of a linear state space model with Gaussian noise, this method is not optimum for a non-linear and non-Gaussian system model. There are many new methods of filtering for the general case. The main emphasis of this thesis is on one such recently developed filter, the particle lter [2,3,6]. In this thesis, a detailed introduction to particle filters is provided as well as some guidelines for the efficient implementation of the particle lter. The application of particle lters to various communication channels like detection of symbols over the channels, capacity calculation of the channel are discussed