Feng-Xiang Ge

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The problem of super-resolution time delay estimation in multipath environments is addressed in this paper. Two cases, active and passive systems, are considered. The time delay estimation is first converted into a sinusoidal parameter estimation problem. Then the sinusoidal parameters are estimated by generalizing the multiple signal classification (MUSIC)(More)
Sparse representation based classification (SRC) is an efficient method with high recognition rate in many pattern recognition applications. Unfortunately, the original SRC method generally requires rigid alignment. In this paper, the feature-based SRC method is addressed by using PCA-SIFT descriptors. The presented method is not only efficient for(More)
Moving target detection and tracking in reverberation environment is an important yet challenging problem in many applications such as speech, sonar, radar and seismic signal processing. Extending the early work of online subspace and sparse filtering [1], this paper presents an approach based on structured convex optimization. Exploiting potentially(More)
Gradient domain optimization is widely used in regularized image super-resolution, in which the gradient of high resolution (HR) is estimated for calculating the regularization energy. In this paper, a progressive gradient estimation (PGE) is proposed. In PGE, the gradient of the reconstructed HR image in the previous round of optimization is taken as the(More)
Multiple-Input Multiple-Output (MIMO) systems have been widely applied to underwater acoustic (UWA) communications with promising results. However, the tradeoff between diversity and multiplexing gains in MIMO systems over underwater acoustic channel is less discussed. In this paper, a quasi-orthogonal group space-time (QoGST) scheme by combining space-time(More)
— The problem of super-resolution time delay estimation in multipath environments is addressed in this paper. Two cases, active and passive systems, are considered. The time delay estimation is first converted into a sinusoidal parameter estimation problem. Then the sinusoidal parameters are estimated by generalizing the Multiple Signal Classification(More)
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