• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.40 no.1
• /
• pp.15-23
• /
• 2003

This paper proposes a fast direction of arrival (DOA) estimation algorithm that can rapidly estimate incidence angles of incoming signals using a pseudo covariance matrix. The conventional subspace DOA estimation methods such as MUSIC (multiple signal classification) algorithms need many sample signals to acquire covariance matrix of input signals. Thus, it is difficult to estimate the DOAs of signals because they cannot perform DOA estimation during receiving sample signals. Also if the D0As of signals are changing rapidly, conventional algorithms cannot estimate incidence angles of signals exactly. The proposed algorithm obtains bearing response and directional spectrum after acquiring pseudo covariance matrix of each snapshot. The incidence angles can be exactly estimated by using the bearing response and directional spectrum. The proposed DOA estimation algorithm uses only concurrent snapshot so as to obtain covariance matrix. Compared to conventional DOA estimation methods. The proposed algorithm has an advantage that can estimate DOA of signal rapidly.

• 제어로봇시스템학회:학술대회논문집
• /
• 1993.10a
• /
• pp.70-75
• /
• 1993

The performance of the BEWE(Bearing Estimation Without Eigendecomposition) algorithm depends on the sensor outputs which are selected to construct the projection matrix. In this paper, we construct the covariance matrix of the bearing estimates for two targets and propose the criterion to select the sensor outputs which minimize the covariance matrix. The computer simulation conforms that the estimation error is smallest when the sensor outputs are selected based on the proposed criterion.

• ETRI Journal
• /
• v.40 no.2
• /
• pp.197-206
• /
• 2018

Multiple-input multiple-output (MIMO) systems offer significant enhancements in terms of their data rate and channel capacity compared to traditional systems. However, correlation degrades the system performance and imposes practical limits on the number of antennas that can be incorporated into portable wireless devices. The use of switched parasitic antennas (SPAs) is a possible solution, especially where it is difficult to obtain sufficient signal decorrelation by conventional means. The covariance matrix represents the correlation present in the propagation channel, and has significant impact on the MIMO channel capacity. The results of this work demonstrate a significant improvement in the MIMO channel capacity by using SPA with the knowledge of the covariance matrix for all pattern configurations. By employing the "water-pouring algorithm" to modify the covariance matrix, the channel capacity is significantly improved compared to traditional systems, which spread transmit power uniformly across all the antennas. A condition number is also proposed as a selection metric to select the optimal pattern configuration for MIMO-SPAs.

• Journal of Welding and Joining
• /
• v.18 no.1
• /
• pp.97-103
• /
• 2000

Time series analysis results show the SVD is a candidate of on-line monitoring of welding penetration when the covariance matrix of a full penetration is used as a mapping function. As the reconstructed embedding vectors from the chaotic scalar time series are manipulated by the covariance matrix, the mapped tim series lie on a hyper-ellipsoid which the lengths of semi-axes are the squared eigenvalues of the covariance matrix in the case of full penetration. These visualize by two dimensional stroboscope views. The other cases like partial penetration, are different in the sense of sizes and shapes. Here we test two types of time series; the ion current and the X-ray. The ion current is better than the X-ray as an on-line monitoring signal, because the difference of the eigenvalue spectrum of the ion(between the pull penetration and partial penetration) is bigger than those of the X-ray.

• MALSORI
• /
• v.55
• /
• pp.93-102
• /
• 2005

In this paper, sub-stream based eigenvoice method is proposed to overcome the weak points of conventional eigenvoice and dimensional eigenvoice. In the proposed method, sub-streams are automatically constructed by the statistical clustering analysis that uses the correlation information between dimensions. To obtain the reliable distance matrix from covariance matrix for dividing into optimal sub-streams, MAP adaptation technique is employed to the covariance matrix of training data and the sample covariance of adaptation data. According to our experiments, the proposed method shows $41\%$ error rate reduction when the number of adaptation data is 50.

• Journal of Korean Society for Quality Management
• /
• v.22 no.1
• /
• pp.54-65
• /
• 1994

Multivariate exponentially weighted moving average (EWMA) control charts for monitoring the variance-covariance matrix are investigated. Two basic approaches, "combine-accumulate" approach and "accumulate-combine" approach, for using past sample information in the developement of multivariate EWMA control charts are considered. Multivariate EWMA control charts for monitoring the variance-covariance matrix are compared on the basis of their average run length (ARL) performances. The numerical results show that multivariate EWMA control charts based on the accumulate-combine approach are more efficient than corresponding multivariate EWMA control charts based on the combine-accumulate approach.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.19 no.2
• /
• pp.345-353
• /
• 1994

This paper proposes a high resolution direction finding method, which is so called the 'averaged MUSIC'. This method uses a new sample array covariance matrix that consists of diagonal components obtained by taking averages of the diagonal component values of the sample covariance matrix for the MUSIC. This paper also shows that the proposed method performs higher resolced direction-of-arrival estimation than the MUSIC in such cases as low signal-to-noise ratio, closed signal sources, and limited number of sensors, based on the statistical analysis.

• Proceedings of the KIEE Conference
• /
• 1992.07a
• /
• pp.453-456
• /
• 1992

A Data-based Covariance Matrix(DCM) is introduced in the Eigenvector(EV) method, among subspace methods of estimating multiple sinusoidal frequencies from finite white noisy measurements. It is shown that the EV with the DCM can obtain the true. frequencies from finite noiseless data Some asymptotic results and further improvement on the DCM are also presented mathematically. Monte-carlo simulations are statistically conducted from the view-points of means and standard deviations in the EV's of DCM and Conventional Covariance Matrix(CCM). Simulations show a great promise for using the DCM, particularly for the cases of short data records, closely spaced frequencies and high signal-to-noise ratios.

• ETRI Journal
• /
• v.36 no.4
• /
• pp.545-553
• /
• 2014

This paper proposes an algorithm to sense orthogonal frequency-division multiplexing (OFDM) signals in cognitive radio (CR) systems. The basic idea behind this study is when a primary user is occupying a wireless channel, the covariance matrix is non-diagonal because of the time domain cross-correlation of the cyclic prefix (CP). In light of this property, a new decision metric that measures the power of the data found on two minor diagonals in the covariance matrix related to the CP is introduced. The impact of synchronization errors on the signal detection is analyzed. Besides this, a likelihood-ratio test is proposed according to the Neyman-Pearson criterion after deriving probability distribution functions of the decision metric under hypotheses of signal presence and absence. A threshold, subject to the requirement of probability of false alarm, is derived; also the probabilities of detection and false alarm are computed accordingly. Finally, numerical simulations are conducted to demonstrate the effectiveness of the proposed algorithm.

• Journal of the Korean Data and Information Science Society
• /
• v.17 no.3
• /
• pp.953-961
• /
• 2006

Let's say that we are given a k number of random variables following Poisson distribution that are individually dependent and which forms multivariate Poisson distribution. We particularly dealt with a method of creating random numbers that satisfies the covariance matrix, where the elements of covariance matrix are parameters forming a multivariate Poisson distribution. To create such random numbers, we propose a new algorithm based on the method reducing the number of parameter set and deal with its relationship to the Park et al.(1996) algorithm used in creating multivariate Bernoulli random numbers.