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Consider the compressed sensing problem of estimating an unknown k-sparse n-vector from a set of m noisy linear equations. Recent work focused on the noise sensitivity of particular algorithms — the scaling of the reconstruction error with added noise. In this paper, we study the minimax noise sensitivity — the minimum is over all possible recovery algorithms and the maximum is over all vectors obeying...
The knowledge of the propagation environment can greatly improve the performance of indoor wireless localization systems, since it can be exploited to cancel out, at least to some extent, the effects of physical obstructions (e.g., walls) degrading the radio signals employed for localization. In indoor localization systems, the propagation environment can be described by maps (e.g., floor plans),...
Detection of defects at early stage is crucial to fault prognostics. Periodic impulses indicate the occurrence of faults in roller bearings. However, it is difficult to detect the impulses of initiating defects because they are rather weak and are often immersed in heavy noise. Existing wavelet threshold de-noising methods are not efficient because they use orthogonal wavelets, which do not match...
Air platforms can localize an emitter in 3-dimensions, by measuring the incident signal's angle-of-arrival (AOA). The resulting localization equations are nonlinear, since they contain arctangent functions of azimuth and elevation and can be computationally complex to solve. By assuming small AOA measurement errors, this paper provides a linear formulation, leading to a closed-form, weighted least...
Estimation of perfusion parameters from dynamic contrast-enhanced ultrasound (DCE-US) data relies on locally fitting mathematical models to the time-echo-power curves derived from a sequence. The least-squares method generally used to fit a parametric perfusion model to experimental data is optimal only under the hypothesis of an additive Gaussian noise. Due to the nature of the DCE-US signal, this...
In this work, time-of-arrival (TOA)-based wireless sensor localization in non-line-of-sight (NLOS) environments is investigated. In such environments, the accuracy of localization techniques is significantly degraded. While previous work often assumes some knowledge of the NLOS environment, we assume that the estimator knows neither which connections are NLOS nor the distribution of the NLOS errors...
Wireless localization in non-line of sight scenarios suffers from ambiguous range measurements. To solve the resulting problems, a novel probabilistic localization approach is investigated in this work. Following up the soft-ranging algorithm from a former work, we define an algorithm for soft-decision based position estimation. In this paper, we focus on the calculation and evaluation of direct-path...
An adaptive minimum selection combining (MSC) scheme is proposed for time varying, intersymbol interference fading communication channel in presence of impulsive noise. The receiver adaptively selects and combines minimum number of diversity branches to reduce the complexity and to save the power. We use the maximum likelihood (ML) metric to choose the branches instead of the instantaneous signal...
In this paper we develop receiver design techniques for shot noise limited multiple-input multiple-output (MIMO) systems, specifically 2×2 MIMO non-line-of-sight (NLOS) free space optical (FSO) or ultraviolet (UV) communication systems, to illustrate the spatial multiplexing advantage of using a MIMO technique. The receivers considered are the zero forcing receiver, the minimum mean square error (MMSE)...
There are many approaches being proposed to find the correspondence points between two images. They generally perform when used to find the correspondences between two images of the same object, such as in a video sequence or in a stereo camera. However, they fail if the number of true matches between two images was small compared to all the potential correspondence points found, which could happen...
This paper focuses on motion estimation using inertial measurements and observations of naturally occurring point features. To date, this task has primarily been addressed using filtering methods, which track the system state starting from known initial conditions. However, when no prior knowledge of the initial system state is available, (e.g., at the onset of the system's operation), the existing...
In this paper, we consider the problem of fusing measurements which contain correlated noises within posegraph-based formulations of filtering and estimation problems. We develop a formulation of the Weighted Geometric Density (WGD) fusion algorithm, a generalisation of Covariance Intersection (CI), for posegraphs. We show that this form can generate covariance consistent estimates. We propose two...
We cast the problem of reverse-engineering the connectivity matrix of genetic regulatory networks from a limited number of measurements as a regularized multivariate regression problem. The regularization term incorporates the prior knowledge of sparsity of genetic regulatory networks. Moreover, the genetic profiles within a measurement are assumed to be correlated with a full covariance structure...
In this paper, we present a novel approach to the problem of estimating and tracking the direction-of-arrival (DOA) of signals with known waveforms and unknown gains impinging on symmetric sparse subarrays. Unlike the conventional methods, which estimate the DOA based on the spatial signature of the signal with known waveform, the proposed method partitions the whole least square (LS) problem into...
Charrelation matrices are similar in structure (and in additional properties) to correlation matrices, and are closely related to Hessians of the log-characteristic function at selected “processing-points” away from the origin. Charrelation-based estimation methods were shown to offer significant improvement over second-order (correlation-based) methods when the latter are suboptimal. However, judicious...
Instances of biological macromolecular complexes that have identical chemical constituents may not have the same geometry due to, for example, flexibility. Cryo electron microscopy provides one noisy projection image of each of many instances of a complex where the projection directions for the different instances are random. The noise is sufficient severe (SNR ≪ 1) that the projection direction for...
With regard to the aircraft flutter flight test stochastic models coexisting input and output observation noise, this paper deduces the simplified form of the maximum likelihood cost function about the stochastic model by virtue of the frequency domain maximum likelihood estimation principle. Then a global optimization iterative convolution smoothing identification method is derived to significantly...
We present an algorithm to dereverberate single-channel audio signals in both noisy and noise-free acoustical environments. Recently, the model-based dereverberation that use the statistical model for room impulse responses (RIRs) is considered to be a fairly attractive approach for reverberant speech, since existing model-based estimators show the late reverberant spectral variance (LRSV) is linear-related...
Many subspace estimation techniques assume either that the system has a calibrated array or that the noise covariance matrix is known. If the noise covariance matrix is unknown, training or other calibration techniques are used to find it. In this paper another approach to the problem of unknown noise covariance is presented. The complex factor analysis (FA) and a new extended version of this model...
In this paper, we consider the detection of a deterministic signal with an unknown scaling amplitude in the presence of a colored noise, when there is a covariance mismatch between the null and alternative hypotheses. Specifically, we consider a scenario where the target incurs an additional subspace interference that is orthogonal to the target steering vector and only present under the alternative...
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