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—This is the second paper in a series of using cognitive radio network as wireless sensor network. The motivation of the paper is to push the convergence of radar and communication systems into a unified cognitive network. This paper studies this vision from a secure point of view. We propose two methods for robust spectrum sensing in the same framework of(More)
—Spectrum sensing is a cornerstone in cognitive radio. Covariance matrix based method has been widely used in spectrum sensing. As is well-known that the covariance matrix of white noise is proportional to the identity matrix which is sparse. On the other hand, the covariance matrix of signal is usually low-rank. Robust principal component analysis (PCA)(More)
Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Recently, cognitive radio and smart grid are two areas which have received considerable research impetus. Cognitive radios are intelligent software defined radios (SDRs) that efficiently utilize the unused regions(More)
—Kernel method is a very powerful tool in machine learning. The trick of kernel has been effectively and extensively applied in many areas of machine learning, such as support vector machine (SVM) and kernel principal component analysis (kernel PCA). Kernel trick is to define a kernel function which relies on the inner-product of data in the feature space(More)
—Spectrum sensing is a fundamental problem in cognitive radio. We propose a function of covariance matrix based detection algorithm for spectrum sensing in cognitive radio network. Monotonically increasing property of function of matrix involving trace operation is utilized as the cornerstone for this algorithm. The advantage of proposed algorithm is it(More)
—Feature template matching (FTM) was proposed by Zhang and Qiu in 2011 for spectrum sensing in cognitive radio. Theoretical analysis for FTM is, however, missing in the literature. This paper will address this issue. Another new direction suggested by this paper is a nonlinear version of FTM, which is called kernel FTM (KFTM). The proposed nonlinear(More)
—This paper describes a sparse imaging approach for estimating change images from a constellation of multistatic radar. In our setup, radar antennas are arranged around the perimeter of a surveillance region. This provides large angular diversity but a very small angular sampling. Conventional backprojection imaging techniques applied to this data produce(More)
—There is a trend of applying machine learning algorithms to cognitive radio. One fundamental open problem is to determine how and where these algorithms are useful in a cognitive radio network. In radar and sensing signal processing, the control of degrees of freedom (DOF)—or dimensionality—is the first step, called pre-processing. In this paper, the(More)