Blind Channel Estimation and Equalization for Multiple FIR Channels

This paper deals with the problem of blind equalizations based on effective channel order determination for multiple FIR channels. Most popular order determination methods use the eigenvalue decomposition (EVD) technique with an overmodeled data correlation matrix. However, performing the EVD consumes huge computation resources. In this paper, we consider the channel with infinite small leading and tailing terms which is natural for measured microwave radio channels, and develop a computationally simple method for effective channel order determination. Based on multiple-shift property of a data correlation matrix, a new performance index is analyzed. The channel order is determined if the performance index is greater than a threshold. To select the threshold, we model the performance index as an -distributed random variable. For a specified confidence level, the threshold can be found from the table. This proposed method does not require EVD, the computation load is much lower than that of the EVD-based methods.</p

Blind Adaptive Channel Equalization with Performance Analysis

A new adaptive multiple-shift correlation (MSC)-based blind channel equalizer (BCE) for multiple FIR channels is proposed. The performance of the MSC-based BCE under channel order mismatches due to small head and tail channel coefficient is investigated. The performance degradation is a function of the optimal output SINR, the optimal output power, and the control vector. This paper also proposes a simple but effective iterative method to improve the performance. Simulation examples are demonstrated to show the effectiveness of the proposed method and the analyses.</p

Application of Near Real-Time and Multiscale Three Dimensional Earth Observation Platforms in Disaster Prevention

Taiwan frequently experiences natural disasters such as typhoons, floods, landslides, debris flows, and earthquakes. Therefore, the instant acquisition of high-definition images and topographic or spatial data of affected areas as disasters occur is crucial for disaster response teams and making emergency aid decisions. The National Applied Research Laboratories has implemented the project “development of near real-time, high-resolution, global earth observation 3D platform for applications to environmental monitoring and disaster mitigation.” This developmental project integrates earth observation techniques, data warehousing, high-performance visualization displays, grids, and disaster prevention technology to establish a near real-time high-resolution three-dimensional (3D) disaster prevention earth observation application platform for Taiwan. The main functions of this platform include (1) integration of observation information, such as Formosat-2 satellite remote sensing, aerial photography, and 3D photography of disaster sites, to provide multidimensional information of the conditions at the affected sites; (2) disaster prevention application technologies, such as large-sized high-resolution 3D projection system, medium-sized active stereo projection systems, and small-sized personal computers with multiscale 3D display systems; (3) a 3D geographical information network platform that integrates data warehousing and cloud services, complies with the Open Geospatial Consortium (OGC) international standard for image data exchange and release processes, and includes image overlaying and added-value analysis of disasters; and (4) near real-time and automated simulation of image processing procedures, which accelerates orthophoto processing once raw data are received from satellites and provides appropriate images for disaster prevention decision-making within 3 to 6 h. This study uses the 88 Flood event of Typhoon Morakot in 2009, Typhoon Fanapi in 2011, and the 311 Earthquake of Japan in 2011 as examples to dissert the applications, functions and features of this platform for supporting disaster response and disaster recovery decision-making