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Estimation of covariance matrices is a fundamental step in hyperspectral remote sensing where most detection algorithms make use of the covariance matrix in whitening procedures. We present a simple method to improve the estimation of the eigenvalues of a sample covariance matrix. With the improved eigenvalues we construct an improved covariance matrix. Our method is based on the Marcenko-Pastur law,...
This paper addresses two issues related to the detection of hyperspectral anomalies. The first issue is the evaluation of anomaly detector performance even when labeled data is not available. The second issue is the estimation of the covariance structure of the data in local detection methods, such as the RX detector, when the number of available training pixels n is not much larger than (and may...
In this paper, we describe a modified recursive least squares (RLS) algorithm for the online identification of switched linear systems (SLSs). For this problem, a real time mode detection (MD) method is usually employed to detect the running mode for each output data and this part is very important for the estimation results. In fact, it is rather difficult to design a perfect MD method that can identify...
The interference rejection combining (IRC) receiver is effective in improving the cell-edge user throughput because it suppresses inter-cell interference. The IRC receiver is typically based on the minimum mean square error (MMSE) criteria, which requires channel estimation and covariance matrix estimation including the inter-cell interference with high accuracy. The paper investigates the gain from...
Here, a novel and efficient moving object detection strategy by non-parametric modeling is presented. Whereas the foreground is modeled by combining color and spatial information, the background model is constructed exclusively with color information, thus resulting in a great reduction of the computational and memory requirements. The estimation of the background and foreground covariance matrices,...
In this paper, we propose to use as an alternative to Extended Kalman Filter Estimator (EKFE), the joint version of Unscented Kalman Filter (UKF) Estimator (UKFE) to solve simultaneously the state and parameter estimation problems, suitable integrated in our proposed Fault Detection and Diagnosis (FDDI) strategy. This strategy is useful to monitor and to control the recent generation of high complexity...
A novel method is introduced in this paper that detects the number of signal sources utilizing the uniformity of MUSUC spectrum. MUSIC scanning spectrum has different uniformity with different noise subspace. The standard deviation is utilized to describe the uniformity of MUSIC scanning spectrum. According to the turn point of the standard deviation curve, the source number is detected and the locations...
Linear frequency modulation (LFM) signals are widely used in information systems. However, using traditional array signal process couldn't commendably estimate DOA of LFM signal, because which is a non-stationary signal. In this paper, the proposed method firstly applied the fractional Fourier Transform (FRFT) to construct the LFM signals array model, in which time-variant steering vector is changed...
The SVD algorithm is an efficient method for the coherent DOA estimation, but it just fits only for the uniform linear array. A method of the coherent DOA estimation based on UCA is proposed in this paper. Through combining model-switching with SVD algorithm, it can improve the performance of decorrelation for uniform circular array. The computer simulations showed that this method has a good performance...
A new decorrelation algorithm through perpendicular translation based on Uniform Circular Array (UCA) has been proposed in this paper. More than one covariance matrices were generated by shifting the array of received signal at two perpendicular directions firstly, and then a new covariance matrix which was a full rank matrix was obtained by averaging the previous matrixes and the signals mentioned...
Simultaneous localization and mapping (SLAM) is one of the challenging issues in recent decades. In this paper solving vision based SLAM problem using Kalman filters family have been provided. It is focused on mobile robot equipped with stereo vision sensor which moves in an indoor environment. The mobile robot navigated among the landmarks which were detected by scale invariant feature transform...
Virtual Array Transformation of DOA Algorithm is widely used in actual engineering DOA estimation of the uniform linear array (ULA) of spacing of array elements greater than half a wavelength or any non-uniform linear array (NULA). However, when virtual array is transformed, if there is a signal outside the current transformation sensor, a serious impact will be had on virtual array DOA estimation...
Optical imaging in vivo is an important tool for allowing researchers to understand neural ensemble interactions during awake behavior, sleep, anesthesia and during seizure activity. A major bottleneck in the overall efficiency of neural imaging experiments is the need for post-hoc analysis of imaging data. Computational capabilities are now at the point where real- or near-real-time multivariate...
Ground based measurements of slant total electron content (TEC) can be assimilated into ionospheric models to produce 3D representations of ionospheric electron density. The Electron Density Assimilative Model (EDAM) has been developed for this purpose. Previous tests using EDAM and ground based data have demonstrated that the information on the vertical structure of the ionosphere is limited in this...
The work presented herein proposes an algorithm for joint Carrier Frequency Offset (CFO) and channel estimation, acquisition and tracking, for OFDM systems in the presence of very high mobility. The algorithm is based on a parametric channel model. Assuming the path delays are known, the multi-path Rayleigh channel Complex Gains (CG) variation, within one OFDM symbol, is approximated by a Basis Expansion...
A new method for improvement of 2-D DOA estimation based on Matrix Pencil (MP) Method is presented. We show that with Tapering of the data matrix, the accuracy of azimuth and elevation estimation can be improved. The method directly applied to the algorithm and only with one snapshot of data, DOA can be estimated. The simulation results demonstrated the effectiveness of the proposed algorithm.
A new pseudo-noise resampling technique is proposed to mitigate the effect of outliers in Root-MUSIC. After resampling of Root-MUSIC via pseudo-randomly generated noise we combine it with conventional beamformer (as a result we obtain the resampled modified root-MUSIC). Censored selection of the results of pseudo-randomly resampled modified Root-MUSIC algorithms is exploited based on an appropriate...
We discuss in the paper the use of the Riemannian mean given by the differential geometric tools. This geometric mean is used in this paper for computing the centers of class in the polarimetric H/α unsupervised classification process. We can show that the centers of class will remain more stable during the iteration process, leading to a different interpretation of the H/α/A classification. This...
In this contribution, the relation between the principal components of the covariance matrix of a hyperspectral image and the spectra of the endmembers is studied. When the data satisfy the spectral mixing model, from this relation the spectra of the endmembers and the abundance of each endmember in the pixels of the image can be theoretically obtained through a non-lineal minimization process. The...
Biomass estimation performance from model-based polarimetric SAR interferometry (PolInSAR) using generic parametric and non-parametric regression methods is evaluated at L- and P-band frequencies over boreal forest. PolInSAR data is decomposed into ground and volume contributions, estimating vertical forest structure, and using a set of obtained parameters for biomass regression. The considered estimation...
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