A novel direction of arrival (DOA) estimation method for the coherent signal is presented in this paper. The proposed method applies the eigenvector associated with max eigenvalue, which contains the DOAs of all signals, to form a Toeplitz matrix, yielding an unconstrained optimization problem. Then, the DOA is obtained by peak searching of the pseudo power spectrum without the knowledge of signal number. It is illustrated that the method has a great performance and low computation complexity for the coherent signal. Simulation results verify the usefulness of the method.

The Jacobi-Davidson method and the Riccati method for eigenvalue problems are studied. In the methods, one has to solve a nonlinear equation called the correction equation per iteration, and the difference between the methods comes from how to solve the equation. In the Jacobi-Davidson/Riccati method the correction equation is solved with/without linearization. In the literature, avoiding the linearization is known as an improvement to get a better solution of the equation and bring the faster convergence. In fact, the Riccati method showed superior convergence behavior for some problems. Nevertheless the advantage of the Riccati method is still unclear, because the correction equation is solved not exactly but with low accuracy. In this paper, we analyzed the approximate solution of the correction equation and clarified the point that the Riccati method is specialized for computing particular solutions of eigenvalue problems. The result suggests that the two methods should be selectively used depending on target solutions. Our analysis was verified by numerical experiments.

This paper proposes a novel blind adaptive array scheme with subcarrier transmission power assignment (STPA) for spectrum superposing in cognitive radio networks. The Eigenvector Beamspace Adaptive Array (EBAA) is known to be one of the blind adaptive array algorithms that can suppress inter-system interference without any channel state information (CSI). However, EBAA has difficulty in suppressing interference signals whose Signal to Interference power Ratio (SIR) values at the receiver are around 0dB. With the proposed scheme, the ST intentionally provides a level difference between subcarriers. At the receiver side, the 1st eigenvector of EBAA is applied to the received signals of the subcarrier assigned higher power and the 2nd eigenvector is applied to those assigned lower power. In order to improve interference suppression performance, we incorporate Beamspace Constant Modulus Algorithm (BSCMA) into EBAA (E-BSCMA). Additionally, STPA is effective in reducing the interference experienced by the primary system. Computer simulation results show that the proposed scheme can suppress interference signals received with SIR values of around 0dB while improving operational SIR for the primary system. It can enhance the co-existing region of 2 systems that share a spectrum.

We study nn random symmetric matrices whose entries above the diagonal are iid random variables each of which takes 1 with probability p and 0 with probability 1-p, for a given density parameter p=α/n for sufficiently large α. For a given such matrix A, we consider a matrix A ' that is obtained by removing some rows and corresponding columns with too many value 1 entries. Then for this A', we show that the largest eigenvalue is asymptotically close to α+1 and its eigenvector is almost parallel to all one vector (1,...,1).

Recently, wireless multi-hop network using MIMO two-way relaying technique has been attracted much attention owing to its high network efficiency. It is well known that the MIMO two-way multi-hop network (MTMN) can provide its maximum throughput in uniform topology of node location. However, in realistic environments with non-uniform topology, network capacity degrades severely due to unequal link quality. Furthermore, the end-to-end capacity also degrades at high SNR due to far (overreach) interference existing in multi-hop relay scenarios. In this paper, we focus on several power allocation schemes to improve the end-to-end capacity performance of MTMN with non-uniform topology and far interference. Three conventional power allocation schemes are reformulated and applied under the system model of MTMN. The first two are centralized methods, i.e., Eigenvector based Power Allocation (EPA) which employs linear algebra and Optimal Power Allocation (OPA) using convex optimization. The last one is Distributed Power Allocation (DPA) using game theory. It is found from numerical analyses that the power allocation schemes are effective for MTMN in terms of end-to-end capacity improvement, especially in non-uniform node arrangement and at high SNR.

This paper proposes an indoor event detection system for homes and offices that is based on electric wave reception such as intrusion into home or office. The proposed system places antenna array on the receiver side and detects events such as intrusion using the eigenvector spanning signal subspace obtained by the antenna array. The eigenvector is based on not received signal strengths (RSS) but direction of arrival (DOA) of incident signals on the antenna array. Therefore, in a static state, the variance of the eigenvector over time is smaller than that of RSS. The eigenvector changes only when the indoor environment of interest changes intermittently and statically, or dynamically. The installation cost is low, because the detection range is wide owing to indoor reflections and diffraction of electric wave and only a pair of transmitter and receiver are used. Experimental results reveal that the proposed method can distinguish the state when no event occurs and that when an event occurs clearly. Since the proposed method has a low false detection rate, it offers higher detection rates than the systems based on RSS.

Both adaptive modulation and diversity combining are attractive techniques to combat fading and these two can be applicable to each digital-modulated symbol in OFDM transmission. In this letter, aiming to combat severe fading more effectively than the adaptive modulation, we theoretically analyze the benefit of a frequency diversity scheme within one OFDM symbol, which is a simple kind of coded OFDM (COFDM) based on IEEE 802.16 protocols. A simple closed form equation of bit error rate (BER) is derived, and then the advantages of correlated diversity gain and interference suppression by the diversity scheme are verified by both theoretical analysis and Monte Carlo simulation.

We propose the construction of an appearance manifold with embedded view-dependent covariance matrix to recognize 3D objects which are influenced by geometric distortions and quality degradation effects. The appearance manifold is used to capture the pose variability, while the covariance matrix is used to learn the distribution of samples for gaining noise-invariance. However, since the appearance of an object in the captured image is different for every different pose, the covariance matrix value is also different for every pose position. Therefore, it is important to embed view-dependent covariance matrices in the manifold of an object. We propose two models of constructing an appearance manifold with view-dependent covariance matrix, called the View-dependent Covariance matrix by training-Point Interpolation (VCPI) and View-dependent Covariance matrix by Eigenvector Interpolation (VCEI) methods. Here, the embedded view-dependent covariance matrix of the VCPI method is obtained by interpolating every training-points from one pose to other training-points in a consecutive pose. Meanwhile, in the VCEI method, the embedded view-dependent covariance matrix is obtained by interpolating only the eigenvectors and eigenvalues without considering the correspondences of each training image. As it embeds the covariance matrix in manifold, our view-dependent covariance matrix methods are robust to any pose changes and are also noise invariant. Our main goal is to construct a robust and efficient manifold with embedded view-dependent covariance matrix for recognizing objects from images which are influenced with various degradation effects.

In a frequency-selective multiple-input multiple-output (MIMO) channel, the optimum transmission is achieved by beamforming with eigenvectors obtained at each discrete frequency point, i.e., an extension of eigenbeam-space division multiplexing (E-SDM). However, the calculation load of eigenvalue decomposition at the transmitter increases in proportion to the number of frequency points. In addition, frequency-independent eigenvectors increase the delay spread of the effective channel observed at the receiver. In this paper, we propose a pseudo eigenvector scheme for the purpose of mitigating the calculation load and maintaining frequency continuity (or decreasing the delay spread). First, we demonstrate that pseudo eigenvectors reduce the delay spread of the effective channels with low computational complexity. Next, the practical performance of the pseudo E-SDM (PE-SDM) transmission is evaluated. The simulation results show that PE-SDM provides almost the same or better performance compared with E-SDM when the receiver employs a time-windowing-based channel estimation available in the low delay spread cases.

In D.O.A. estimation, identification of the signal and the noise subspaces plays an essential role. This identification process was traditionally achieved by the eigenvalue decomposition (EVD) of the spatial correlation matrix of observations or the generalized eigenvalue decomposition (GEVD) of the spatial correlation matrix of observations with respect to that of an observation noise. The framework based on the GEVD is not always an extension of that based on the EVD, since the GEVD is not applicable to the noise-free case which can be resolved by the framework based on the EVD. Moreover, they are not applicable to the case in which the spatial correlation matrix of the noise is singular. Recently, a quotient-singular-value-decomposition-based framework, that can be applied to problems with singular noise correlation matrices, is introduced for noise reduction. However, this framework also can not treat the noise-free case. Thus, we do not have a unified framework of the identification of these subspaces. In this paper, we show that a unified framework of the identification of these subspaces is realized by the concept of proper and improper eigenspaces of the spatial correlation matrix of the noise with respect to that of observations.

A multiple-source system for rendering the sound pressure distribution in a target region can be modeled as a multi-input-multi-output (MIMO) system with the inputs being the source strengths and the outputs being the pressures on multiple measuring points/sensors. In this paper, we propose a target-oriented acoustic radiation generation technique (TARGET) for sound field control. For the MIMO system of a given geometry, a series of basic radiation modes, namely, target-oriented radiation modes (TORMs) can be derived using eigenvector analysis. Different TORMs have different contributions to the system control gain, which is defined as the ratio of the acoustic energy generated in the target zone to the transmitter output power. The TARGET can be effectively applied to the sound reproduction and suppression, which correspond the generations of bright and dark zone respectively. In acoustically bright zone generation and sound beamforming, the highest-gain TORM can be employed to determine the optimal source strengths. In active noise control, the strengths of the secondary sources can be derived using low-gain TORMs. Simulation results show that the proposed method has better or comparable performance than the traditional techniques.

To achieve a very high data rate within a limited frequency band in orthogonal frequency division multiplexing (OFDM) systems, multi-input multi-output (MIMO) techniques are very promising. Moreover, if a transmitter has the channel state information (CSI), the achievable spectrum efficiency can be maximized using the eigenbeam-space division multiplexing (E-SDM). However, this scheme demands accurate channel estimation. Therefore, in a closed-loop transmission scheme, an increase in the amount of feedback is absolutely necessary for the E-SDM. This paper describes a downlink beamforming method that significantly reduces the amount of feedback needed by using the common transmission weight vectors in all sub-carriers, compared to the amount required for E-SDM. The proposed method also applies transmission diversity to compensate for the quality. The effectiveness of the proposed method was confirmed using computer simulations in both Ricean and Rayleigh fading environments.

This paper proposes a novel pre-FFT type OFDM adaptive array antenna called "Eigenvector Combining." The eigenvector combining array antenna is a realization of a post-FFT type OFDM adaptive array antenna through a pre-FFT signal processing, so it can achieve excellent performance with less computational complexity and shorter training symbols. Numerical results demonstrate that the proposed eigenvector combining array antenna shows excellent bit error rate performance close to the lower bound just with 2 OFDM symbol-long training symbols.

The present letter deals with the blind equalization problem of a single-input single-output infinite impulse response (SISO-IIR) channel with additive Gaussian noise. To solve the problem, we propose a new criterion for maximizing constrainedly a fourth-order cumulant. The algorithms derived from the criterion have such a novel property that even if Gaussian noise is added to the output of the channel, an effective zero-forcing (ZF) equalizer can be obtained with as little influence of Gaussian noise as possible. To show the validity of the proposed criterion, some simulation results are presented.

An approach based on signal subspace analysis is proposed to blind estimation of the PN (Pseudo Noise) sequence from lower SNR (Signal to Noise Ratios) DS/SS (Direct Sequence Spread Spectrum) signals. The received signal is divided into vectors according to a temporal window, from which an autocorrelation matrix is computed and accumulated. The PN sequence can be reconstructed from principal eigenvectors of the matrix.

This paper presents some effective methods for improving the performance of a speaker identification system. Based on the multiresolution property of the wavelet transform, the input speech signal is decomposed into various frequency subbands in order not to spread noise distortions over the entire feature space. For capturing the characteristics of the vocal tract, the linear predictive cepstral coefficients (LPCC) of the lower frequency subband for each decomposition process are calculated. In addition, a hard threshold technique for the lower frequency subband in each decomposition process is also applied to eliminate the effect of noise interference. Furthermore, cepstral domain feature vector normalization is applied to all computed features in order to provide similar parameter statistics in all acoustic environments. In order to effectively utilize all these multiband speech features, we propose a modified vector quantization as the identifier. This model uses the multilayer concept to eliminate the interference among the multiband speech features and then uses the principal component analysis (PCA) method to evaluate the codebooks for capturing a more detailed distribution of the speaker's phoneme characteristics. The proposed method is evaluated using the KING speech database for text-independent speaker identification. Experimental results show that the recognition performance of the proposed method is better than those of the vector quantization (VQ) and the Gaussian mixture model (GMM) using full-band LPCC and mel-frequency cepstral coefficients (MFCC) features in both clean and noisy environments. Also, a satisfactory performance can be achieved in low SNR environments.

This paper describes an efficient face recognition method using a combination of the Radon transform and the KL expansion. In this paper, each facial image is transformed into many sets of line integrals resulting from the Radon transform in 2D space. Based on this transformation, a new face-recognition method is proposed by using many subspaces generated from the vector spaces of the Radon transform. The efficiencies of the proposed method are proved by the classification rate of 100% in the experimental results, and the reduction to O(n4) instead of O(n6) of the operation complexity in KL(Karhunen-Loeve) expansion, where n is the size of sample images.

In Ref.[5], the author defines "approximate eigenvalues" and "approximate eigenvectors," which are, in short, Taylor series expansions of eigenvalues and eigenvectors of a polynomial matrix. In this paper, an efficient algorithm to compute the approximate eigenvalues and eigenvectors is presented. The algorithm performs the computations with an arbitrary degree of convergence.

This paper proposes a new digital beamforming adaptive array antenna (DBFAAA) that is effective in severe multipath environments in which timing and carrier synchronization circuits cannot function ideally resulting in the DBFAAA losing control. The proposed DBFAAA has two stages. In the first, the DBFAAA captures the desired signal and establishes synchronization. In the second, the DBFAAA optimizes the beam pattern of the signal. The proposed configuration employs an eigenvector beam of the maximum eigenvalue in the first stage beam-forming. In addition, a fractionally-spaced-tapped-delay-line (FS-TDL) with real tap weights, which is placed after the beam-former, is applied to achieve timing synchronization. The behavior of the proposed DBFAAA for asynchronous sampling data is investigated and the results indicate that the proposed configuration enables asynchronous sampling at the A/D converter. A prototype of the proposed DBFAAA achieving 38-Mbps real-time data communication is introduced and the transmission performance is shown.

A method for locating the minimum eigenvalue and its corresponding eigenvector is considered. The core procedure utilized is the modified Rayleigh quotient iteration (MRQI). The convergence rate of the Rayleigh quotient iteration (RQI) is cubic. However, unfortunately, the RQI may not always locate the minimum eigenvalue. In this paper, a new MRQI that can always locate the minimum eigenpair is given. Based on the MRQI, a fast algorithm to locate minimum eigenpair will be proposed. This method has the following characteristics. First, it does not compute the inclusion interval. Second, it works for any Hermitian matrix as well as Toeplitz matrix. Third, it works on matrices having more than one minimum eigenvalue. Fourth, the numerical error of this method is very small. Fifth, it is attractively simple and fast. The convergence rate of this method is asymptotically cubic. MATLAB simulation results show that this method may outperform other methods. The term MRQI has been already used. Differences in several MRQI methods are discussed. Mathematical properties of the MRQI are investigated. This research can be effectively applied to diverse field of the signal processing including communication, because the signal space can be efficiently obtained.