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Outlier detection is an important topic in machine learning and has been used in a wide range of applications. In this paper, we approach outlier detection as a binary-classification issue by sampling potential outliers from a uniform reference distribution. However, due to the sparsity of data in high-dimensional space, a limited number of potential outliers may fail to provide sufficient information to assist the classifier in describing a boundary that can separate outliers from normal data effectively. To address this, we propose a novel Single-Objective Generative Adversarial Active Learning (SO-GAAL) method for outlier detection, which can directly generate informative potential outliers based on the mini-max game between a generator and a discriminator. Moreover, to prevent the generator from falling into the mode collapsing problem, the stop node of training should be determined when SO-GAAL is able to provide sufficient information. But without any prior information, it is extremely difficult for SO-GAAL. Therefore, we expand the network structure of SO-GAAL from a single generator to multiple generators with different objectives (MO-GAAL), which can generate a reasonable reference distribution for the whole dataset. We empirically compare the proposed approach with several state-of-the-art outlier detection methods on both synthetic and real-world datasets. The results show that MO-GAAL outperforms its competitors in the majority of cases, especially for datasets with various cluster types or high irrelevant variable ratio. The experiment codes are available at: https://github.com/leibinghe/GAAL-based-outlier-detection

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A photonics-based radar with generation and de-chirp processing of broadband linear frequency modulated continuous-wave (LFMCW) signal in optical domain is proposed for high-resolution and real-time inverse synthetic aperture radar (ISAR) imaging. In the proposed system, a broadband LFMCW signal is generated by a photonic frequency quadrupler based on a single integrated electro-optical modulator, and the echoes reflected from the targets are de-chirped to a low frequency signal by a microwave photonic frequency mixer. The proposed radar can operate at a high frequency with a large bandwidth, and thus achieve an ultra-high range resolution for ISAR imaging. Thanks to the wideband photonic de-chirp technique, the radar receiver could apply low-speed analog-to-digital conversion and mature digital signal processing, which makes real-time ISAR imaging possible. A K-band photonics-based radar with an instantaneous bandwidth of 8 GHz (18-26 GHz) is established and its performance for ISAR imaging is experimentally investigated. Results show that a recorded two-dimensional imaging resolution of ~2 cm × ~2 cm is achieved with a sampling rate of 100 MSa/s in the receiver. Besides, fast ISAR imaging with 100 frames per second is verified. The proposed radar is an effective solution to overcome the limitations on operation bandwidth and processing speed of current radar imaging technologies, which may enable applications where high-resolution and real-time radar imaging is required.