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This paper is devoted to the research of methods and experiments of regularization deconvolution theory on the azimuth super-resolution of forward-looking imaging radar. L1 norm is usually used as a regular term to obtain a stable solution due to its strong resolving power for sparse targets. However, deconvolution is an ill-posed problem, in the process of deconvolution iteration, using the L1 norm as a regular term is sensitive to noise and may causes a large deviation between the solution and the true value due to the influence of noise. This paper proposes the super-resolution imaging method combined truncation singular value decomposition (TSVD) with L1 norm constraint. At lower SNR, this method effectively solves the problem of noise amplification during deconvolution iteration. The effectiveness and advancement of the proposed algorithm are verified by simulation results.

Since many target features can be acquired via inverse synthetic aperture radar (ISAR), the key of implementing deception jamming on ISAR lies in inducing decoy images imitating the scattering features of real target. In this paper, based on the combination of the frequency diverse array (FDA) and scatter-wave jamming (SWJ), an approach producing multiple two-dimensional decoy images with real target features is presented, the distribution and number of which can be arbitrarily manipulated through designing FDA parameters. Details of the jamming on ISAR is provided along with the simulation results, which illuminates the feasibility and validity of the proposed method.

Most of the existing deception jamming methods can only achieve effective jamming on one dimension. Thus, a two-dimensional deception jamming method for SAR imaging based on the frequency diverse array (FDA) and micro-motion modulation is proposed in this paper. The proposed method can produce a number of false targets in both the range and azimuth of SAR image, the location and number of which can be specifically controlled. Based on SAR imaging geometry, principles of the jamming are derived. According to the final image expression, jamming performance with different parameters are demonstrated. Experimental results illuminate the feasibility and effectiveness of the proposed method.

There are two major issues in the current Trojan detection system: some of them can not detect unknown Trojans and many of them have low detection rate. To solve these problems, a Trojan horse detection model of nonlinear SVM based on an effective feature selection optimization algorithm is presented in this paper. In this model, we extract the API (application program interface) calls sequence of an executable program as a feature vector and use the feature selection optimization algorithm to choose High-sensitive characteristics which are quantized into data recognized by SVM to build the SVM feature vector library. SVM classifier is trained with the training dataset to find the optimal separating hyper plane. Experiment results demonstrate that this model named PMI-SVM is more effective and steady.

Effective extraction of human area from human-body moving gait image sequences is of great concern to human-body moving gait recognition. A new segmentation algorithm was proposed, the concept of the time-domain was afresh defined, the inverse transformation of the wavelet was unnecessary. The two value result which was gained by combining background subtraction and symmetric frame difference performed moving estimation, it was the new method of time-domain segmentation; the first order wavelet transform was used to the current frame image, used high order linear interpolation to extend the LL weight of wavelet transformation as the same size as the current frame image, watershed segmentation algorithm was used to divide the LL weight image which was extended into many closed and non-overlapping regions, it was space-domain segmentation; used spatio-temporal combination algorithm, marked the result of space-domain segmentation casting shadow to the result of time-domain segmentation, then gained the accurate area of human-body. The experiments were made on the NLPR gait database and the results proved that the algorithm can abstract the whole area of human-body precisely, owned the good robust noise and flexibility, and eventually improved the performance of gait recognition.