Image classification plays a vital role in the field of computer vision. Many existing image classification methods with high accuracy are based on supervised learning, which requires a great number of labeled images. However, the labeling of images requires a lot of human and material resources. In this paper, we focus on semi-supervised image classification, which can build a classifier using a few labeled images and plenty of unlabeled images. We propose an attention-based generative adversarial network (GAN) for semi-supervised image classification, which can capture global dependencies and adaptively extract important information. Furthermore, we apply spectral normalization, which can stabilize the training of attention-based GAN. The experimental results obtained with the CIFAR-10 dataset show that the proposed method is comparable with the state-of-the-art GAN-based semi-supervised image classification methods.
For coherent integration detection of ultrafast maneuvering targets with modern radar, a novel long-time coherent integration algorithm, Polynomial Rotation-Polynomial Fourier Transform (PRPFT), is proposed to compensate across range unit range walk (RW) and Doppler frequency migration (DFM) simultaneously caused by super-high speed and strong maneuvering. First, RW can be corrected by the polynomial rotation transform (PRT) via rotating the coordinate locations of echo data. Then, the polynomial Fourier transform (PFT) can realize the compensation of DFM and coherent integration. To reduce the computational complexity, one decision method is proposed to search the multidimensional parameter space. Finally, numerical experiments are provided to validate the effectiveness of the proposed method.
As an effective method in signal reconstruction model, compressed sensing (CS) has achieved excellent performance in sparse array reconstruction. However, it is necessary to set the penalization factor before iterative calculation, which will increase the difficulty to convergence the result to the global optimal solution. In this paper, we remove the process of choosing penalization factor and reconstruction error by modifying the iterative expression as well as alternating direction method of multipliers (ADMM) algorithm respectively. In addition, the improved model is shown to be convex and thus can be solved using the CVX toolbox. Simulation result shows that the reference pattern could be reconstructed with minimum number of antenna elements by the proposed algorithms. Moreover, the proposed methods have significant performance improvement in main sidelobe level (MSL).
Proc. SPIE. 11384, Eleventh International Conference on Signal Processing Systems
KEYWORDS: Target detection, Radar, Edge detection, Statistical analysis, Detection and tracking algorithms, Sensors, Monte Carlo methods, Optical character recognition, Signal detection, Environmental sensing
Constant false alarm rate (CFAR) detectors are widely used in modern radar system to declare the presence of targets. Due to the serious masking effects under the multiple targets situation and the clutter edge, the detection probability of CFAR detectors decrease sharply and the alarm rates increase significantly. To solve these problems, a robust adaptive amplitude iteration CFAR (AAI-CFAR) algorithm is proposed in this paper and obtains good performance. By combining the 2nd-order statistic, variability index, and the 4th-order statistic, kurtosis, a variable scaling factor is designed in the amplitude iteration to adapt different environment. Plenty of Monte Carlo simulations are applied to evaluate the performance of the proposed method under different clutter scenarios compared with existing CFAR detectors, which illustrate the superiority and robustness of AAI-CFAR.