Search Results (7)

Robust adaptive beamforming (RAB) is essential in many applications to ensure signal-receiving quality when model errors exist. Eigen-subspace projection (ESP), one of the most popular RAB methods, can be used when there are arbitrary model errors. However, a major challenge of ESP is projection subspace selection. Traditional ESP (TESP) treats the signal subspace as the projection subspace; thus, source enumeration is required to obtain prior information. Another inherent defect is its poor performance at low signal-to-noise ratios (SNRs). To overcome these drawbacks, two improved ESP-based RAB methods are proposed in this study. Considering that a reliable signal-of-interest steering vector needs to be obtained via the subspace projection, the main idea underlying the proposed methods is to use sequenced steering vector estimation to invert the subspace dimension estimate for an arranged eigenvector matrix. As the proposed methods do not require source enumeration, they are simple to implement. Numerical examples demonstrate the effectiveness and robustness of the proposed methods in terms of output signal-to-interference-plus-noise ratio performance. Specifically, compared with TESP, the proposed methods present at least a 2.6 dB improvement at low SNRs regardless of the error models. Full article

For the free vibrations of multi-degree mechanical structures appeared in structural dynamics, we solve the quadratic eigenvalue problem either by linearizing it to a generalized eigenvalue problem or directly treating it by developing the iterative detection methods for the real and complex eigenvalues. To solve the generalized eigenvalue problem, we impose a nonzero exciting vector into the eigen-equation, and solve a nonhomogeneous linear system to obtain a response curve, which consists of the magnitudes of the n-vectors with respect to the eigen-parameters in a range. The n-dimensional eigenvector is supposed to be a superposition of a constant exciting vector and an m-vector, which can be obtained in terms of eigen-parameter by solving the projected eigen-equation. In doing so, we can save computational cost because the response curve is generated from the data acquired in a lower dimensional subspace. We develop a fast iterative detection method by maximizing the magnitude to locate the eigenvalue, which appears as a peak in the response curve. Through zoom-in sequentially, very accurate eigenvalue can be obtained. We reduce the number of eigen-equation to $n-1$ to find the eigen-mode with its certain component being normalized to the unit. The real and complex eigenvalues and eigen-modes can be determined simultaneously, quickly and accurately by the proposed methods. Full article

When the desired signal and multiple mainlobe interferences coexist in the received data, the performance of the current mainlobe interference suppression algorithms is severely challenged. This paper proposes a multiple mainlobe interference suppression method based on eigen-subspace and eigen-oblique projection to solve this problem. First, use the spatial spectrum algorithm to calculate interference power and direction. Next, reconstruct the eigen-subspace to accurately calculate the interference eigenvector, then generate the eigen-oblique projection matrix to suppress mainlobe interference and output the desired signal without distortion. Finally, the adaptive weight vector is calculated to suppress sidelobe interference. Through the above steps, the proposed method solves the problem that the mainlobe interference eigenvector is difficult to select, caused by the desired signal and the mismatch of the mainlobe interference steering vector and its eigenvector. The simulation result proves that our method could suppress interference more successfully than the former methods. Full article

Synthetic Aperture Radar (SAR) is a high-resolution radar that operates all day and in all weather conditions, so it has been widely used in various fields of science and technology. Ship detection using SAR images has become important research in marine applications. However, in complex scenes, ships are easily submerged in sea clutter, which cause missed detection. Due to this, strong sidelobes in SAR images generate false targets and reduce the detection accuracy. To solve these problems, a ship detection method based on eigensubspace projection (ESSP) in SAR images is proposed. First, the image is reconstructed into a new observation matrix along the azimuth direction, and the phase space matrix of the reconstructed image is constructed by using the Hankel characteristic, which preliminarily determines the approximate position of the ship. Then, the autocorrelation matrix of the reconstructed image is decomposed by eigenvalue decomposition (EVD). According to the size of the eigenvalues, the corresponding eigenvectors are divided into two parts, which constitute the basis of the ship subspace and the clutter subspace. Finally, the original image is projected into the ship subspace, and the ship data in the ship subspace are rearranged to obtain the precise position of the ship with significantly suppressed clutter. To verify the effectiveness of the proposed method, the ESSP method is compared with other detection methods on four images at different sea conditions. The results show that the detection accuracy of the ESSP method reaches 89.87% in complex scenes. Compared with other methods, the proposed method can extract ship targets from sea clutter more accurately and reduce the number of false alarms, which has obvious advantages in terms of detection accuracy and timeliness. Full article

Secure user access to devices and datasets is widely enabled by fingerprint or face recognition. Organization of the necessarily large secure digital object datasets, with objects having content that may consist of images, text, video or audio, involves efficient classification and feature retrieval processing. This usually will require multidimensional methods applicable to data that is represented through a family of probability distributions. Then information geometry is an appropriate context in which to provide for such analytic work, whether with maximum likelihood fitted distributions or empirical frequency distributions. The important provision is of a natural geometric measure structure on families of probability distributions by representing them as Riemannian manifolds. Then the distributions are points lying in this geometrical manifold, different features can be identified and dissimilarities computed, so that neighbourhoods of objects nearby a given example object can be constructed. This can reveal clustering and projections onto smaller eigen-subspaces which can make comparisons easier to interpret. Geodesic distances can be used as a natural dissimilarity metric applied over data described by probability distributions. Exploring this property, we propose a new face recognition method which scores dissimilarities between face images by multiplying geodesic distance approximations between 3-variate RGB Gaussians representative of colour face images, and also obtaining joint probabilities. The experimental results show that this new method is more successful in recognition rates than published comparative state-of-the-art methods. Full article

The presence of spoofing signals poses a significant threat to global navigation satellite system (GNSS)-based positioning applications, as it could cause a malfunction of the positioning service. Therefore, the main objective of this paper is to present a spatial-temporal technique that enables GNSS receivers to reliably detect and suppress spoofing. The technique, which is based on antenna array, can be divided into two consecutive stages. In the first stage, an improved eigen space spectrum is constructed for direction of arrival (DOA) estimation. To this end, a signal preprocessing scheme is provided to solve the signal model mismatch in the DOA estimation for navigation signals. In the second stage, we design an optimization problem for power estimation with the estimated DOA as support information. After that, the spoofing detection is achieved by combining power comparison and cross-correlation monitoring. Finally, we enhance the genuine signals by beamforming while the subspace oblique projection is used to suppress spoofing. The proposed technique does not depend on external hardware and can be readily implemented on raw digital baseband signal before the despreading of GNSS receivers. Crucially, the low-power spoofing attack and multipath can be distinguished and mitigated by this technique. The estimated DOA and power are both beneficial for subsequent spoofing localization. The simulation results demonstrate the effectiveness of our method. Full article

In this study, we present a third-order tensor-based multilinear eigenECG (MEECG) and multilinear Fisher ECG (MFECG) for individual identification based on the information obtained by an electrocardiogram (ECG) sensor. MEECG and MFECG are based on multilinear principal component analysis (MPCA) and multilinear linear discriminant analysis (MLDA) in the field of multilinear subspace learning (MSL), respectively. MSL directly extracts features without the vectorization of input data, while MSL extracts features without vectorizing the input data while maintaining most of the correlations shown in the original structure. In contrast with unsupervised linear subspace learning (LSL) techniques such as PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis), it is less susceptible to small-data problems because it learns more compact and potentially useful representations, and it can efficiently handle large tensors. Here, the third-order tensor is formed by reordering the one-dimensional ECG signal into a two-dimensional matrix, considering the time frame. The MSL consists of four steps. The first step is preprocessing, in which input samples are centered. The second step is initialization, in which eigen decomposition is performed and the most significant eigenvectors are selected. The third step is local optimization, in which input data is applied by eigenvectors from the second step, and new eigenvectors are calculated using the applied input data. The final step is projection, in which the resultant feature tensors after projection are obtained. The experiments are performed on two databases for performance evaluation. The Physikalisch-Technische Bundesanstalt (PTB)-ECG is a well-known database, and Chosun University (CU)-ECG is directly built for this study using the developed ECG sensor. The experimental results revealed that the tensor-based MEECG and MFECG showed good identification performance in comparison to PCA and LDA of LSL. Full article