Cognitive Radar Detection in Nonstationary Environments and Target Tracking

Target detection and tracking are the most fundamental and important problems in a wide variety of defense and civilian radar systems. In recent years, to cope with complex environments and stealthy targets, the concept of cognitive radars has been proposed to integrate intelligent modules into conventional radar systems. To achieve better performance, cognitive radars are designed to sense, learn from, and adapt to environments. In this dissertation, we introduce cognitive radars for target detection in nonstationary environments and cognitive radar networks for target tracking.For target detection, many algorithms in the literature assume a stationary environment (clutter). However, in practical scenarios, changes in the nonstationary environment can perturb the parameters of the clutter distribution or even alter the clutter distribution family, which can greatly deteriorate the target detection capability. To avoid such potential performance degradation, cognitive radar systems are envisioned which can rapidly recognize the nonstationarity, accurately learn the new characteristics of the environment, and adaptively update the detector. To achieve this cognition, we propose a unifying framework that integrates three functions: (i) change-point detection of clutter distributions by using a data-driven cumulative sum (CUSUM) algorithm and its extended version, (ii) learning/identification of clutter distribution by using kernel density estimation (KDE) methods and similarity measures (iii) adaptive target detection by automatically modifying the likelihood-ratio test and the corresponding detection threshold. We also conduct extensive numerical experiments to show the merits of the proposed method compared to a nonadaptive case, an adaptive matched filter (AMF) method, and the clairvoyant case.For target tracking, with remarkable advances in sensor techniques and deployable platforms, a sensing system has freedom to select a subset of available radars, plan their trajectories, and transmit designed waveforms. Accordingly, we propose a general framework for single target tracking in cognitive networks of radars, including joint consideration of waveform design, path planning, and radar selection. We formulate the tracking procedure using the theories of dynamic graphical models (DGM) and recursive Bayesian state estimation (RBSE). This procedure includes two iterative steps: (i) solving a combinatorial optimization problem to select the optimal subset of radars, waveforms, and locations for the next tracking instant, and (ii) acquiring the recursive Bayesian state estimation to accurately track the target. Further, we use an illustrative example to introduce a specific scenario in 2-D space. Simulation results based on this scenario demonstrate that the proposed framework can accurately track the target under the management of a network of radars

Latent-Variable Modeling: Algorithms, Inference, and Applications

Many driving factors of physical systems are often latent or unobserved. Thus, understanding such systems crucially relies on accounting for the influence of the latent structure. This thesis makes advances in three aspects of latent-variable modeling: inference, algorithms, and applications. Specifically, we develop and explore latent-variable techniques that a) ensure interpretable and statistically significant models, b) can be efficiently optimized to identify best fit to data, and c) provide useful insights in real-world applications. The specific contributions of this thesis are: 1. We employ a latent-variable graphical modeling technique to develop the first state-wide statistical model of the California reservoir network. With this model, we precisely characterize the system-wide behavior of the network to hypothetical drought conditions, and proposed guidelines for more sustainable reservoir management. 2. Motivated by the previous application, we provide a geometric framework to assess the extent to which our latent variable model has learned true or false discoveries about the relevant physical phenomena. Our approach generalizes the classical notions of true and false discoveries in mathematical statistics that rely on the discrete structure of the decision space to settings where the decision space is continuous and more complicated. We highlight the utility of this viewpoint in problems involving subspace selection and low-rank estimation. 3. We propose a convex optimization procedure to fit a latent-variable graphical model for generalized linear models. This framework provides a flexible approach to model non-Gaussian variables including Poisson, Bernoulli, and exponential variables. A particularly novel aspect of our formulation is that it incorporates regularizers that are tailored to the type of latent variables. 4. We describe a computationally efficient framework to learn a latent-variable model with high-dimensional and non-iid data. This framework is based on factoriable precision operators that decouple the component associated with the observational dependencies and the component associated to interdependencies among the variables. 5. We propose a convex optimization technique to provide semantics to latent variables of a factor model. This approach is based on linking auxiliary variables -- chosen based on domain expertise -- to these latent variables.</p

Recent Advances in Social Data and Artificial Intelligence 2019

The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace

Scientific Assessment of Climate Change and Its Effects in Maine

Climate change has already made its presence known in Maine, from shorter winters and warmer summers with ocean heat waves, to stronger storms, new species showing up in our backyards and the Gulf of Maine, aquatic algal blooms, acidic ocean waters that affect shellfish, and new pests and diseases that harm our iconic forests and fisheries. The health of Maine people is also being affected by climate change, from high heat index days driving increased emergency room visits to the ravages of Lyme and other vector-borne diseases. And our economy is feeling the effects, too -with farmers trying to adapt to longer growing seasons but dealing with severe storms and late frosts, aquaculturists already adapting to a more acidic ocean, and winter sports like skiing and snowmobiling being impacted by our shrinking winter season. This is the first report from the Maine Climate Council’s Scientific and Technical Subcommittee, produced by more than 50 scientists from around the State representing Scientific and Technical Subcommittee members, other co-authors, and contributors. This report is part of the 2020 Maine Climate Action Plan. The report summarizes how climate change has already impacted Maine and how it might continue affecting our State in the future. The findings from this report inform the ongoing deliberations of the Maine Climate Council and have aided the Maine Climate Council’s six working groups in the development of draft strategies to address climate change by reducing Maine’s greenhouse gas emissions. In addition, the Scientific and Technical Subcommittee identified critical scientific information gaps and needs to better understand and forecast potential future climate change impacts in the State. Key take-aways from this report are listed below, with the full details appearing in each of the twelve chapters