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Spectrum sensing is a key technology for the cognitive radio. Since orthogonal frequency-division multiplexing (OFDM) is one of the major wideband transmission techniques, spectrum sensing for OFDM based primary signals using its sample covariance matrix is developed. In this paper, two sensing methods based the sample covariance matrix are proposed: one is based on testing hypothesis in which the...
The estimated covariance matrix is corrupted by the interference-target signals (outliers) in nonhomogeneous clutter environments, which leads the conventional space-time adaptive processing (STAP) to be degraded significantly in clutter suppression. Therefore, a robust generalized inner products (GIP) algorithm by utilizing prolate spheroidal wave functions (PSWF) is proposed to eliminate the outliers...
A fast and efficient algorithm is proposed to estimate the direction of arrival (DOA) of the signals impinging on an uniformly located linear array. Unlike the classical MUSIC method, it is not needed for the method proposed in this paper to have the number of the signal sources known as a prior knowledge. The proposed method only needs the forward recursions of multistage Wiener filter (MSWF) to...
This paper is concerned with assessing localization errors emanating from the image registration of two monochromatic fluorescence microscopy images. Assuming an affine transform exists between images, registration in this setting typically involves using control points to solve a multivariate linear regression problem; however with measurement errors existing in both sets of variables the use of...
The unscented Kalman filter (UKF) was recently introduced in literature for simultaneous multi-tensor estimation and tractography. This UKF however was not intrinsic to the space of diffusion tensors. Lack of this key property leads to inaccuracies in the multi-tensor estimation as well as in tractography. In this paper, we propose an novel intrinsic unscented Kalman filter (IUKF) in the space of...
A novel finite impulse response (FIR) adaptive filter algorithm was proposed for system identification based on independent component analysis (ICA). It shows an excellent robustness for non-Gaussian disturbance. In this paper, we discuss various properties of this ICA-based adaptive filter algorithm, including the role of the scaling parameter, the local stability condition and a performance analysis...
In the recent past considerable research has been performed on blind signal detection techniques that exploit the covariance matrix of the signals received at a cognitive radio (CR). These techniques overcome the noise uncertainty problem of the energy detection (ED) method and can even perform better than ED for correlated signals. Contrary to the previous work where the main evaluation technique...
This paper is concerned with the maximization of the weighted sum-rate in the multicell MIMO multiple access channel (MAC). Considered is the multicell network operating on the same frequency channel with multiple mobile stations (MS) per cell. Assuming the interference coordination mode in the multicell network, each base-station (BS) only decodes the signals for the MSs within its cell. However,...
A sensor network's motes observe the environment, make estimates based on observations with spatially correlated noise sources, and then send/relay these estimates to a Cluster-Head (CH). A novel scheme based on dithered quantization and channel compensation is used to ensure that each mote's local estimate received by the CH is unbiased. Based on an upper bound of the noise covariance matrix, the...
Randomized (dithered) quantization is a method capable of achieving white reconstruction error independent of the source. Dithered quantizers have traditionally been considered within their natural setting of uniform quantization. In this paper we extend conventional dithered quantization to nonuniform quantization, via a subterfage: dithering is performed in the companded domain. Closed form necessary...
Granger causality considers the question of whether two time series exert causal influences on each other. Causality testing usually relies on prediction, i.e., if the prediction error of the first time series is reduced by taking measurements from the second one into account, then the latter is said to have a causal influence on the former. In this paper, a nonparametric framework based on functional...
This paper is motivated by the challenge of high fidelity processing of images using a relatively small set of projection measurements. This is a problem of great interest in many sensing applications, for example where high photodetector counts are precluded by a combination of available power, form factor and expense. The emerging methods of dictionary learning and compressive sensing offer great...
Matrix factorization from a small number of observed entries has recently garnered much attention as the key ingredient of successful recommendation systems. One unresolved problem in this area is how to adapt current methods to handle changing user preferences over time. Recent proposals to address this issue are heuristic in nature and do not fully exploit the time-dependent structure of the problem...
In our previous work, we developed efficient field reconstruction methods in wireless sensor networks. In this paper, we use an amplify-and-forward to transmit the sensor measurements to the fusion center and we derive the mean square error (MSE) of the reconstructed field as a function of the measurement and receive SNR and of the sensor positions. We propose to allocate the sensor node transmit...
The work presents the development and evaluation of uncertainty-based range measurement selection criteria for active sensing within a localization scenario. The simulated setup consists of a mobile unit equipped with a gyroscope, an incremental encoder, and being capable to measure the distance to a finite number of stationary anchors with known positions and associated position uncertainties. The...
The problem of source enumeration in array processing is investigated. In an information theoretic criterion framework, we use in addition to the probability density function of observations, the probability density function of the sample eigenvalues obtained from the sample covariance matrix of the observations. Although the latter adds information to the criterion it is widely ignored by most traditional...
This paper deals with different techniques for linear equalization of multipath channels with imperfect channel estimation (CE). We develop a unified framework based on Krylov subspace expansion, which allows us to compare the performance of the conjugate gradient (CG) method, diagonal loading (DL), and a hybrid scheme. Our analysis shows that the DL method generally outperforms its alternatives,...
This paper introduces new minor (noise) subspace tracking (MST) algorithms based on the minimum noise subspace (MNS) technique. The latter has been introduced as a computationally efficient subspace method for blind system identification. We exploit here the principle of the MNS, to derive the most efficient algorithms for MST. The proposed method joins the advantages of low complexity and fast convergence...
This paper addresses the problem of principal subspace tracking in presence of a colored noise. We propose to extend the YAST algorithm to handle such a case. We also propose a Riemannian framework that could benefit to other classical trackers. Finally, as a proof of concept, our method is compared to the only oblique tracker of the literature on a toy dataset.
The paper presents a fully distributed framework for sequential recursive state estimation in inter-connected electrical power systems. Specifically, the setup considered involves a grid partitioned into multiple control areas that communicate over a sparse communication network. In the absence of a global sensor data fusion center (the conventional centralized SCADA) and with sensing model uncertainties,...
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