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Wave energy extraction technologies are rapidly maturing, leading to increased expectations that they will soon be integrated into the electrical grid. This paper reviews the state of the art technologies along with system level modeling while providing an initial study of the impacts of integration on a typical electrical grid network as compared to the closest related technology, wind energy extraction...
Consider a multiple-input multiple-output skywave over-the-horizon (MIMO-OTH) radar system withM transmit and N receive antennas employing the conventional optimal detector for a single complex Gaussian target. The signal from the mth transmit antenna reaches the target after being reflected by the ionosphere via Qm ray paths. Each of these multipath signals bounce off the target and reach the nth...
Taking into account the existence of multipath ionospheric propagation (MIP), this paper develops the received signal model for a non-point target for multiple-input multiple-output skywave over-the-horizon (MIMO-OTH) radar for the first time. The model describes the ionospheric state, the number of propagation paths between a radar antenna and the target center, as well as the statistics of the reflection...
Ionospheric information is required when estimating target parameters in skywave over-the-horizon (OTH) radar. Unlike the traditional OTH radar which uses only the measurements of ionospheric parameters obtained from an ionosonde to estimate the target parameters, the multiple-input multiple-output skywave OTH (MIMO-OTH) radar studied in this paper estimates the ionospheric and target parameters jointly...
Waveform design is studied for a multiple-input multiple-output over-the horizon (MIMO-OTH) radar system faced with a combination of additive Gaussian noise and signal dependent clutter. We define the waveform transmitted at each antenna as a weighted sum of a set of orthogonal sequences. Assume that different transmit antennas use the same set of orthogonal sequences, while the weighting factors...
Distributed estimation using quantized data in the presence of Byzantine attacks is considered. Several subsets of sensors are assumed to be tampered with by different adversaries. Under the control of adversaries, the compromised sensors transmit fictitious data to the fusion center (FC) in order to undermine the estimation performance of the sensor network. First, we show that it is possible to...
In this paper, attacks on systems estimating the value of an unknown deterministic parameter based on quantized noisy observations are studied. The attacks are categorized according to the information available to the attacking entity. The considered categories are attacks of full information, which are generally more devastating, and the information free attacks, which are generally less effective...
Bayesian bounds incorporate prior knowledge on parameters of interest. Nonlocal bounds can provide more accurate prediction of the performance of estimators over the full range of possible mean-squared errors. For example, local bounds, such as the Cramer-Rao bound (CRB), provide especially inaccurate predictions under low signal-to-clutter-plus-noise ratio (SCNR) conditions. In this paper, we derive...
While recent advances have provided extremely efficient distributed methods for computing optimal test statistics for many hypothesis testing problems occurring in large sensor networks, the popular multivariate Gaussian hypothesis testing problem involving a change in both the mean vector and covariance matrix is not one of these. The difficultly is that these test statistics generally require long...
Advanced control systems for Wave Energy Converter (WEC) devices require the prediction of the exact time waveforms of ocean waves. In this paper, ocean waves are described as a sum of several plane waves of different frequencies and directions of travel while sensor measurements are assumed to be observed under Gaussian noise. A general expression for the Cramer Rao Bound (CRB) of the estimates is...
Exploiting the received signals, which are usually not employed in the traditional OTH radar for ionospheric parameter estimation, we study joint estimation of target and ionospheric parameters for multiple-input multiple-output skywave OTH (MIMO-OTH) radar. We employ a parabolic layer model to characterize the ionospheric distortions. We model the target and ionospheric parameters as deterministic...
We study the waveform design problem for a multiple-input multiple-output over-the-horizon (MIMO-OTH) radar system faced with a combination of additive Gaussian noise and signal dependent clutter. Considering the operational frequency of the MIMO-OTH radar is generally limited to a certain frequency band due to propagation and implementation issues, the waveform transmitted at each antenna is constructed...
This work investigates the target localization capabilities of a multiple input multiple output (MIMO) radar with multiple widely separated transmit and receive sensor arrays. The corresponding Cramer-Rao lower bound (CRLB) is derived for noncoherent estimation of a target's location. The derived CRLB can be seen as a generalization of the CRLB for a monostatic radar with a single sensor array. It...
In this paper, we consider the problem of waveform design for a multiple-input multiple-output over-the-horizon (MIMO-OTH) radar system corrupted by colored Gaussian noise and signal dependent clutter. The discrete prolate spheroidal (DPS) sequences are applied to construct the waveforms as their band-limited property is suitable for addressing the operational frequency limitations of the MIMO-OTH...
Previous research has justified the use of a state space model for describing smart grid system dynamics. A fault or intrusion in the grid can be found by recognizing a change in system matrices of the state space model. A new hypothesis testing-based approach using the Locally Optimum Unknown Direction (LOUD) test has been proposed to detect possible changes. Previous work has shown that the LOUD...
Previous work on ordered transmission approaches showed significant transmission savings but focused entirely on cases with statistically independent observations at a set of sensor nodes. Here we take the first steps toward applying ordering to cases with statistically dependent observations. While we focus on a particular signal detection problem, we choose one of the most well studied problems,...
We consider fault detection through apparent changes in the bus susceptance parameters of modern power grids. We formulate the problem using a linear errors-invariables model and derive its corresponding generalized likelihood ratio (GLRT) based on the total least squares (TLS) methodology. Next, we propose a competing detection technique based on the recently proposed total maximum likelihood (TML)...
Using a linear DC power flow model, the power flow calculation can be determined by the bus susceptance matrix and the voltage angles. In this paper, we consider a power system where the voltage angle measurement at each bus is transmitted to a control center via a wireless communication channel. In order to model unsuccessful transmissions, we derive a two-component Gaussian mixture model to describe...
This paper studies the distributed change detection problem in Gaussian graphical models (GGMs). Statistical analysis in GGM leads to several advantages, including a smaller number of parameters to model a large scale distribution, less samples required for the detection, faster detection and less communication costs. We formulate the hypothesis testing problem for change detection in GGMs and propose...
In order to reduce the number of transmissions between a set of sensors and a fusion center in signal detection applications, we propose an algorithm based on ordering and halting the transmissions wisely, which can reduce the data transmission, and thus expended energy and data rate, without sacrificing signal detection performance. Here we consider the specific case of noncoherent signal detection,...
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