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

• Journal of Advanced Navigation Technology
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• v.14 no.1
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• pp.80-86
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• 2010

Compressive Sensing (CS) has been widely studied as a promising technique in many applications. The CS theory tells that a signal that is known to be sparse in a specific basis can be reconstructed using convex optimization from far fewer samples than traditional methods use. In this paper, we apply CS technique to Multiple-input multiple-output (MIMO) radar systems which employ uniform linear arrays (ULA). Especially, we investigate the problem of finding the direction-of-arrival (DOA) using CS technique and compare the performance with the conventional adaptive MIMO techniques. The results suggest the CS method can provide the similar performance with far fewer snapshots than the conventional adaptive techniques.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.4
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• pp.388-394
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• 2016

There have been many studies on the direction-of-arrival(DOA) estimation algorithm using antenna arrays. Beamforming, Capon's method, maximum likelihood, MUSIC algorithms are the main algorithms for the DOA estimation. Recently, compressive sensing-based DOA estimation algorithm exploiting the sparsity of the incident signals has attracted much attention in the signal processing community. In this paper, the performance of the L1-SVD algorithm, which is based on fitting of the data matrix, is compared with that of the MUSIC algorithm.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.249-250
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• 2006

Nowadays adaptive technique allows arrays of any geometry to be used with fast direction-of-arrival (DOA) estimators designed for linear arrays. So the interpolation of data from a given antenna array onto the output of a virtual array is needed before the direction finding technique is applied to the outputs of a uniform linear virtual array (ULVA). In this paper some superresoluntion methods are used to estimate DOA by best-fit transformation matrix T under different nonuniformly spaced array.

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

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.6
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• pp.48-59
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• 2000

Eigendecomposition based direction-of-arrival(DOA) estimation algorithm such as MUSIC(multiple signal classification) is known to perform well and provide high resolution in white noise environment. However, its performance degrades severely when the noise process is not white. In this paper we consider the DOA estimation problem in a colored noise environment as a problem of extracting periodic signals from noise, and we take the problem to the wavelet domain. Covariance matrix of multiscale components which are obtained by taking wavelet decomposition on the noise has a special structure which can be approximated with a banded sparse matrix. Compared with noise the correlation between multiscale components of narrowband signal decays slowly, hence the covariance matrix does not have a banded structure. Based on this fact we propose a DOA estimation algorithm that transforms the covariance matrix into wavelet domain and removes noise components located in specific bands. Simulations have been carried out to analyze the proposed algorithm in colored noise processes with various correlation properties.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.3
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• pp.1-6
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• 2003

It is difficult to detect and to track the moving targets like tanks and diesel vehicles due to the variety of terrain and moving of targets. It is possible to be happened the aliasing conditions as the difficulty of antenna deployment in the complex environment like the battle fields. In this paper, we study the problem of detecting and tracking of moving targets which are emitting wideband signals under severe spatial aliasing conditions because of the sparse arrays. We developed a direction of arrival(DOA) estimation algorithm based on subband MUSIC(Multiple Signal Classification) method which produces high-resolution estimation. In this algorithm, the true bearings are invariant regardless of changes of frequency bands while the aliased false bearings vary. As a result, the proposed algorithm overcomes the aliasing effects and improves the localization performance in sparse passive arrays.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.6
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• pp.2777-2784
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• 2011

In this paper, DOA(direction of arrival) of multiple incident signals received from linear array antenna and circular array antenna, which is based on nonparametric estimation algorithm, and adaptive beam-forming algorithm are studied and analyzed. In nonparametric estimation algorithm, we minimize a regularized objective function for finding a estimate of the signal energy as a function of angle, using nonquadratic norm which leads to supper resolution and noise suppression. And then, DOA is estimated by the signal and noise spatial steering vector, and adaptive beam-forming pattern is improved by weight vectors obtained from the spatial vector. Especially, the discrimination ability of DOA and the adaptive beam-forming ability according to antenna array methods and the number of array elements are compared and considered.

• The Journal of the Korea Contents Association
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• v.10 no.12
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• pp.34-42
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• 2010

The DOA(direction of arrival), which is based on parametric and nonparametric estimation algorithm, and adaptive beamforming algorithm for mobile communication environments are researched and analyzed. In parametric estimation algorithm, eigenvalues of the signal component and the noise component are obtained from correlation matrix of received signal by array antenna and power spectrum of the received signal is discriminated from them. Otherwise, in nonparametric estimation algorithm, we minimize a regularized objective function for finding a estimate of the signal energy as a function of angle, using nonquadratic norm which leads to supper resolution and noise suppression. And then, DOA is estimated by the signal and noise spatial steering vector, and adaptive beam-forming pattern is improved by weight vectors obtained from the spatial vector. Therefore, the improved directional estimation algorithm with regularizing sparsity constraints offers super-resolution and noise suppression compared to other algorithms.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.687-690
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• 1999

Many high-resolution algorithms based on the eigen-decomposition analysis of observed covariance matrix, such as MVE, MUSIC, and EVM, have been proposed. However, the resolution of spectral estimates for these algorithms is severely degraded when Signal-to-Noise Ratio (SNR) is low and arrival angles of signal are close to each other. And EVM and MUSIC is powerful for the characteristic of SNR. But have the limitation that the number of signals presented is known. While MVE is bad the characteristic of SNR. In this study, we propose a modified MVE to enhance the resolution for Direction-Of-Arrival (DOA) estimation of underwater acoustic signal. This is to remove the limitation that existing algorithms should know the information for the number of signals. Because the algorithms founded on the eigen value estimate DOA with only the noise subspace, they have the high-resolution characteristic. And then, with the method reducing the effect of the signal subspace, we are to reduce the degradation because of complementary relationship between the signal subspace and the noise subspace. This paper, with using the simulation data, we have estimated the proposed algorithms, compared it with other high-resolution algorithms. The simulation results show that the modified MVE proposed is accurate and has a better resolution even though SNR is low, under the same condition.