Electromagnetic signal emitted by satellite communication (satcom) transmitters are used to identify specific individual uplink satcom terminals sharing the common transponder in real environment, which is known as specific emitter identification (SEI) that allows for early indications and warning (I&W) of the targets carrying satcom furnishment and furthermore the real time electromagnetic situation awareness in military operations. In this paper, the authors are the first to propose the identification of specific transmitters of satcom by using probabilistic neural networks (PNN) to reach the goal of target recognition. We have been devoted to the examination by exploring the feasibility of utilizing the Hilbert transform to signal preprocessing, applying the discrete wavelet transform to feature extraction, and employing the PNN to perform the classification of stationary signals. There are a total of 1000 sampling time series with binary phase shift keying (BPSK) modulation originated by five types of satcom transmitters in the test. The established PNNs classifier implements the data testing and finally yields satisfactory accuracy at 8 dB(±1 dB) carrier to noise ratio, which indicates the feasibility of our method, and even the keen insight of its application in military. KEYWORDS classification, feature extraction, probabilistic neural networks, specific emitter identification 1 | INTRODUCTION Early indications and warning (I&W) of the targets emitting active electromagnetic waves is a critical process for electronic warfare (EW) prior to the large-scale military operation. 1,2The emergence of the satellite communication (satcom) not only brings the variety of the ground terminals making highly efficient multiple access points available, but also tremendously broadens the battlefield to ocean-to-ocean. Due to the fact that hundreds of uplink satcom terminals (eg, aircrafts, fixed bases, mobile bases, and vessels or other marine terminals, see Figure 1) are sharing the common satellite, even the identical transponder, although, the identification of the signals becomes the last mile issue for the system of I&W. [3][4][5][6][7][8] For the past decades, the identification of specific communication emitter have enjoyed the most attentions in the area of electronic counter measure activities, resulting to the sustainable prosperity of Measurement and Signature Intelligence (MASINT) in tactical, operational, and strategic situations. 9-11 This work could stretch back to the 1960s when the US government planned to lead the identification and tracking of unique mobile transmitters, which brought about indirectly the emergence of specific emitter identification (SEI) technique. [12][13][14][15][16][17][18][19][20] By virtue of analyzing quantitatively the unintentional modulation details from sampling data (eg, intermodulation distortion, spectrum regrowth, and harmonic generation), rather than the conventional estimated parameters such as frequency, amplitude, and phase (or even the pulse period and width in radar...
Low-light image enhancement is generally regarded as a challenging task in image processing, especially for the complex visual tasks at night or weakly illuminated. In order to reduce the blurs or noises on the low-light images, a large number of papers have contributed to applying different technologies. Regretfully, most of them had served little purposes in coping with the extremely poor illumination parts of images or test in practice. In this work, the authors propose a novel approach for processing low-light images based on the Retinex theory and generative adversarial network (GAN), which is composed of the decomposition part for splitting the image into illumination image and reflected image, and the enhancement part for generating high-quality image. Such a discriminative network is expected to make the generated image clearer. Couples of experiments have been implemented under the circumstance of different lighting strength on the basis of Converted See-In-the-Dark (CSID) datasets, and the satisfactory results have been achieved with exceeding expectation that much encourages the authors. In a word, the proposed GAN-based network and employed Retinex theory in this work have proven to be effective in dealing with the low-light image enhancement problems, which will benefit the image processing with no doubt.
To obtain personalized outcomes for the low-light image enhancement, a novel interactive algorithm based on the well-designed Gamma Curve is proposed to enrich the enhancement techniques. Different from the previous works trying to enhance the image in solely brightness or naturalness by a specific designed deep network, the proposed method is capable of controlling the output results according to the user’s preferences by the same framework with different parameters. There would be three main advantages brought by the proposed network: 1) Interactivity, which allows to generate enhancements results according to users’ preferences in human-interactive manners; 2) Convenience, wherein the model only needs to train for once without using any reference images, and then can obtain results with different brightness during testing by adjusting the hyper-parameter. 3) Fastness, which results from the lightweight network and the excellent properties of the Gamma Curve to make the network operate in extraordinary high speed. Experiments demonstrate the superiority of our algorithm relative to the previous work. In addition, a multi-platform low-illumination enhancement software is explored to facilitate its application for the public.
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