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In this paper we consider “multidimensional” lattice filters (LF) which are based on generalized block both algorithm and factorization of Levinson for various STAP applications with signal's block representation. We unmask the specificity of matrix parameters of multidimensional LF and show the feasibility and appropriateness of their practical implementation on the basis of “one-dimensional” LF,...
When the desired signal is present in the training snapshots, the performance of conventional orthogonal projection (OP) adaptive beamforming degrades severely due to the desired signal cancellation effect. To overcome this deficiency, the improved orthogonal projection (IOP) adaptive beamforming by using reconstructed interference covariance matrix is proposed. In the proposed algorithm, the interference...
The goal of this analysis is the separation of artificial sound from the ambient or background soundscape in real-time via singular value decomposition of a 3×3 covariance matrix obtained from the set of vector measurements sourced from a particle velocity sensor. The summary noise power is then computed for each octave band, and this is used to detect the presence of artificial, polluting sound sources.
Traceable calibration of high-speed communications test equipment face challenges. The propagation of uncertainty from primary waveform standards can be done by using a covariance matrix approach, which becomes impractical as the waveform data length increases. With the new compressed covariance matrix approach uncertainty can be propagated for long waveform records. A freely available software tool...
In order to meet the stereo vision navigation of the autonomous robot, stereo vision initial pose estimation based on unit quaternion is put forward and pose estimation accuracy is predicted and analysed. The research provides basis for further improving the pose estimation accuracy of robot. Gauss-distributed error covariance matrix propagation model was adopted to deduce the error propagation formulas...
Now a days research is going on to design a high performance automatic face recognition system which is really a challenging task for researchers. As faces are complex visual stimuli that differ dramatically, hence developing an efficient computational approach for accurate face recognition is very difficult. In this paper a high performance face recognition algorithm is developed and tested using...
Independent component analysis (ICA) is a statistical signal processing technique for separation of mixed voices, images and signal. The basic idea of ICA is to find the underlying independent components in the mixture by searching for a linear or nonlinear transformation and minimizing the statistical dependence between components. Due to the computational complexity of ICA and commonly used data...
A recent trend in computer vision is to represent images through covariance matrices, which can be treated as points on a special class of Riemannian manifolds. A popular way of analysing such manifolds is to embed them in Euclidean spaces, a process which can be interpreted as warping the feature space. Embedding manifolds is not without problems, as the manifold structure may not be accurately preserved...
We propose a new action and gesture recognition method based on spatio-temporal covariance descriptors and a weighted Riemannian locality preserving projection approach that takes into account the curved space formed by the descriptors. The weighted projection is then exploited during boosting to create a final multiclass classification algorithm that employs the most useful spatio-temporal regions...
This paper introduces a sequentially motivated approach to processing streams of images from datasets with low memory demands. We utilize fuzzy clustering as an incremental dictionary learning scheme and explain how the corresponding membership functions can be subsequently used in encoding features for image patches. We focus on replicating the codebook learning and classification stages from an...
Temporal evolution in the generative distribution of nonstationary sequential data is challenging to model. This paper presents a method for retaining the information in prior distributions of matrix variate dynamic linear models (MVDLMs) as the eigenspace of sequential data evolves. The method starts by constructing sliding windows â" matrices composed of a fixed number of columns...
A non-coherent vector delay/frequency-locked loop architecture for GNSS receivers is proposed. Two dynamics models are considered: PV (position and velocity) and PVA (position, velocity, and acceleration). In contrast with other vector architectures, the proposed approach does not require the estimation of signals amplitudes. Only coarse estimates of the carrier-to-noise ratios are necessary.
This paper proposes a weighted total least squares approach based on both pseudorange and carrier phase measurements. The paper makes use of the weighted total least squares solution to solve the global positioning system (GPS) navigation equation determining the user position. The total least squares estimation considers both measurements vector and observable data matrix errors which common least...
A Gaussian multiple-input multiple-output (MIMO) wiretap channel model is considered, where there exists a transmitter, a legitimate receiver and an eavesdropper each equipped with multiple antennas. The optimality of beamforming for secrecy capacity subject to sum power constraint is studied, and two sufficient conditions for beamforming to be globally optimal are given. The first sufficient condition...
Deployed high-latency anonymous communication systems conceal communication patterns using pool mixes as building blocks. These mixes are known to be vulnerable to Disclosure Attacks that uncover persistent relationships between users. In this paper we study the performance of the Least Squares Disclosure Attack (LSDA), an approach to disclosure rooted in Maximum Likelihood parameter estimation that...
Gaussian Mixture Models (GMMs) are powerful tools for probability density modeling and soft clustering. They are widely used in data mining, signal processing and computer vision. In many applications, we need to estimate the parameters of a GMM from data before working with it. This task can be handled by the Expectation-Maximization algorithm for Gaussian Mixture Models (EM-GMM), which is computationally...
In this paper, frequency-domain subspace-based algorithms are proposed to estimate discrete-time cross-power spectral density (cross-PSD) and auto-power spectral density (auto-PSD) matrices of vector auto-regressive moving-average and moving-average (ARMAMA) models from sampled values of the Welch cross-PSD and auto-PSD estimators on uniform grids of frequencies. The proposed algorithms are shown...
Consensus-based distributed estimation schemes are becoming increasingly popular in sensor networks due to their scalability and fault tolerance capabilities. In a consensus-based state estimation framework, multiple neighboring nodes iteratively communicate with each other, exchanging their own local estimates of a target's state with the goal of converging to a single state estimate over the entire...
In this paper, application-oriented experiment design formulated as a chance constrained problem is investigated. The chance constraint is based on the presumption that the estimated model can be used in an application to achieve a given performance level with a prescribed probability. The aforementioned performance level is dictated by the particular application of interest. The resulting optimization...
As more and more functionalities are packed into a single product, one-response-at-a-time correlation analysis is no longer sufficient to discover critical factors that result in poor qualities or a low yield. Though methodologies of many-to-many correlation analysis have been proposed in the literature, difficulties arise, especially when there exist multi-collinearity effects among variables, to...
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