• Korean Journal of Remote Sensing
• /
• v.38 no.5_2
• /
• pp.737-746
• /
• 2022

Utilizing weather independent SAR images along with machine learning based object detector is effective in robust vessel monitoring. While conventional SAR images often applied amplitude data from Single Look Complex, exploitation of polarimetric parameters acquired from multiple polarimetric SAR images was yet to be implemented to vessel detection utilizing machine learning. Hence, this study used four polarimetric parameters (H, p₁, DoP, DPRVI) retrieved from eigen-decomposition and two backscattering coefficients (γ₀, _VV, γ₀, _VH) from radiometric calibration; six bands in total were respectively exploited from 52 Sentinel-1 SAR images, accompanied by vessel training data extracted from AIS information which corresponds to acquisition time span of the SAR image. Evaluating different cases of combination, the use of polarimetric indexes along with amplitude values derived enhanced vessel detection performances than that of utilizing amplitude values exclusively.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.13 no.3
• /
• pp.165-170
• /
• 2013

In spatial, desired target direction of arrival estimation is to find a incidental signal direction on receive antennas. In this paper, we were an estimation a desired target direction of arrival using maximum likelihood method. Direction of arrival estimation method estimated a desired target calculating the maximum likelihood sensitivity using singular value decomposition above threshold signals among receive signals in maximum likelihood method. Through simulation, we were analysis a performance to compare existing method and proposal method. In direction of arrival estimation, proposed method is effectivity to decrease processing time because it is not doing an eigen decomposition in direction of arrival estimation, and desired target correctly estimated. We showed that proposal method improve more target estimation than general method.

• Wind and Structures
• /
• v.37 no.3
• /
• pp.191-209
• /
• 2023

This work investigates the subcritical free-shear prism wake at Re=22,000 by the Koopman analysis using the Dynamic Mode Decomposition (DMD) algorithm. The Koopman model linearized nonlinearities in the stochastic, homogeneous anisotropic turbulent wake, generating temporally orthogonal eigen tuples that carry meaningful, coherent structures. Phenomenological analysis of dominant modes revealed their physical interpretations: Mode 1 renders the mean-field dynamics, Modes 2 describes the roll-up of the Strouhal vortex, Mode 3 describes the Bloor-Gerrard vortex resulting from the Kelvin-Helmholtz instability inside shear layers, its superposition onto the Strouhal vortex, and the concurrent flow entrainment, Modes 6 and 10 describe the low-frequency shedding of turbulent separation bubbles (TSBs) and turbulence production, respectively, which contribute to the beating phenomenon in the lift time history and the flapping motion of shear layers, Modes 4, 5, 7, 8, and 9 are the relatively trivial harmonic excitations. This work demonstrates the Koopman analysis' ability to provide insights into free-shear flows. Its success in subcritical turbulence also serves as an excellent reference for applications in other nonlinear, stochastic systems.

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

• Korean Journal of Remote Sensing
• /
• v.23 no.2
• /
• pp.89-101
• /
• 2007

This paper investigates the effects of the fusion of multifrequency (C and L bands) polarimetric SAR data in land-cover classification. NASA JPL AIRSAR C and L bands data were used to supervised classification in an agricultural area to simulate the integration of ALOS PALSAR and Radarsat-2 SAR data to be available. Several scattering features derived from target decomposition based on eigen value/vector analysis were used as input for a support vector machines classifier and then the posteriori probabilities for each frequency SAR data were integrated by applying a probabilistic ratio model as a decision level fusion methodology. From the case study results, L band data had the proper amount of penetration power and showed better classification accuracy improvement (about 22%) over C band data which did not have enough penetration. When all frequency data were fused for the classification, a significant improvement of about 10% in overall classification accuracy was achieved thanks to an increase of discrimination capability for each class, compared with the case of L band Shh data.

• Wind and Structures
• /
• v.25 no.3
• /
• pp.301-328
• /
• 2017

Multivariate fluctuating pressures acting on a 2:1 rectangular section (2-D) with dimensions of 9 cm by 4.5 cm has been studied using wind tunnel experiments under uniform and smooth flow condition for various angles of wind incidence. Based on the variation of mean pressure coefficient distributions along the circumference of the rectangular section with angle of wind incidence, and with the aid of skin friction coefficients, three distinct flow regimes with two transition regimes have been identified. Further, variations of mean drag and lift coefficients, Strouhal number with angles of wind incidence have been studied. The applicability of Universal Strouhal number based on vortex street similarity of wakes in bluff bodies to the 2:1 rectangular section has been studied for different angles of wind incidence. The spatio-temporal correlation features of the measured pressure data have been studied using Proper Orthogonal Decomposition (POD) technique. The contribution of individual POD modes to the aerodynamic force components, viz, drag and lift, have been studied. It has been demonstrated that individual POD modes can be associated to different physical phenomena, which contribute to the overall aerodynamic forces.

• The Journal of the Acoustical Society of Korea
• /
• v.37 no.6
• /
• pp.499-505
• /
• 2018

Time delay estimation between two acoustic sensors is widely used in room acoustics and sonar for target position estimation, tracking and synchronization. A cross-correlation based method is representative for the time delay estimation. However, this method does not have enough consideration for the noise added to the receiving acoustic sensors. This paper proposes a new time delay estimation method considering the added noise on the receiver acoustic sensors. From comparing with the existing GCC (Generalized Cross Correlation) method, and adaptive eigen decomposition method, we show that the proposed method outperforms other methods for a colored signal source in the white Gaussian noise condition.

• Journal of Broadcast Engineering
• /
• v.17 no.6
• /
• pp.924-932
• /
• 2012

Decomposition of a stereo signal into the primary and ambient components is a key step to the stereo upmix and it is often based on the principal component analysis (PCA). However, major shortcoming of the PCA-based method is that accuracy of the decomposed components is dependent on both the primary-to-ambient power ratio (PAR) and the panning angle. Previously, a modified PCA was suggested to solve the PAR-dependent problem. However, its performance is still dependent on the panning angle of the primary signal. In this paper, we proposed a new PCA-based primary-ambient decomposition algorithm whose performance is not affected by the PAR as well as the panning angle. The proposed algorithm finds scale factors based on a criterion that is set to preserve the powers of the mixed components, so that the original primary and ambient powers are correctly retrieved. Simulation results are presented to show the effectiveness of the proposed algorithm.

• The Journal of the Acoustical Society of Korea
• /
• v.26 no.3
• /
• pp.123-128
• /
• 2007

This Paper Presents new transient signal classification algorithms for underwater transient signals. In general. the ambient noise has small spectral deviation and energy variation. while a transient signal has large fluctuation. Hence to detect the transient signal, we use the spectral deviation and power variation. To classify the detected transient signal. the feature Parameters are obtained by using the Wigner-Ville distribution based eigenvalue decomposition. The correlation is then calculated between the feature vector of the detected signal and all the feature vectors of the reference templates frame-by-frame basis, and the detected transient signal is classified by the frame mapping rate among the class database.

• Proceedings of the IEEK Conference
• /
• 2003.07c
• /
• pp.2737-2740
• /
• 2003

This paper has been studied an algorithm for EMG signal amplitude estimation in noisy environment. The proposed method has the first stage decomposing the row vector from the delayed EMG signal and the second stage computing the eigenvalues by the eigen decomposition from the covariance matrix of the EMG signal matrix. The last stage is the estimation of RMS values from the eigenvalues. The proposed method was effective when the amplitude of the EMG signal is small, which means the signal to noise ratio is low.