• Journal of electromagnetic engineering and science
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• v.11 no.4
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• pp.250-256
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• 2011

This paper applies a compressed algorithm to improve the spectrum sensing performance of cognitive radio technology. At the fusion center, the recovery error in the analog to information converter (AIC) when reconstructing the transmit signal from the received time-discrete signal causes degradation of the detection performance. Therefore, we propose a subspace pursuit (SP) algorithm to reduce the recovery error and thereby enhance the detection performance. In this study, we employ a wide-band, low SNR, distributed compressed sensing regime to analyze and evaluate the proposed approach. Simulations are provided to demonstrate the performance of the proposed algorithm.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.3
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• pp.267-274
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• 2009

Recently, UAVs (Unmanned Aerial Vehicles) are expected much as the Unmanned Systems for various missions. These missions are often based on the Vision System. Especially, missions such as surveillance and pursuit have a process which is carried on through the transmitted vision data from the UAV. In case of small UAVs, monocular vision is often used to consider weights and expenses. Research of missions performance using the monocular vision is continued but, actually, ground and target model have difference in distance from the UAV. So, 3D distance measurement is still incorrect. In this study, Mean-Shift Algorithm, Optical Flow and Subspace Method are posed to estimate the relative depth. Mean-Shift Algorithm is used for target tracking and determining Region of Interest (ROI). Optical Flow includes image motion information using pixel intensity. After that, Subspace Method computes the translation and rotation of image and estimates the relative depth. Finally, we present the results of this study using images obtained from the UAV experiments.

• Communications for Statistical Applications and Methods
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• v.13 no.2
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• pp.233-241
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• 2006

Classical principal component analysis (PCA) can be formulated as finding the linear subspace that best accommodates multidimensional data points in the sense that the sum of squared residual distances is minimized. As alternatives to such LS (least squares) fitting approach, we produce LMS (least median of squares) and LTS (least trimmed squares)-type PCA by minimizing the median of squared residual distances and the trimmed sum of squares, in a similar fashion to Rousseeuw (1984)'s alternative approaches to LS linear regression. Proposed methods adopt the data-driven optimization algorithm of Croux and Ruiz-Gazen (1996, 2005) that is conceptually simple and computationally practical. Numerical examples are given.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.2
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• pp.580-592
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• 2012

In this paper, a new compressive feedback (CF) scheme based on distributed compressed sensing (DCS) for time-corrected MIMO channel is proposed. First, the channel state information (CSI) is approximated by using a subspace matrix, then, the approximated CSI is compressed using a compressive matrix. At the base station, the approximated CSI can be robust recovered with simultaneous orthogonal matching pursuit (SOMP) algorithm by using forgone CSIs. Simulation results show our proposed DCS-CF method can improve the reliability of system without creating a large performance loss.