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Automatic waveform recognition of overlapping low probability of intercept (LPI) radar signals is an important and challenging task in electronic reconnaissance of the increasingly complicated spectrum environment. In this article, an overlapping LPI waveform recognition processing framework incorporating residual attention-aided U-net generative adversarial network (GAN) and multiinstance multilabel (MIML) classifier is proposed. This framework includes five cascade modules and can achieve satisfactory recognition performance by training with single type of signals only. First, the training signals are transformed into time–frequency images. Then, a residual attention U-net GAN (RAUGAN) with residual learning is employed to reconstruct signal images from noise-contaminated images and with the supervision of the high-quality ones. After that, an instance generation module with asymmetric convolutions generates instance representations, which are then fed into the subsequent residual attention MIML classifier (RAMIML). Finally, an adaptive threshold calibration module is implemented to obtain appropriate thresholds for the final decision. Besides, two loss functions are elaborately designed for the RAUGAN and RAMIML, respectively. Extensive experimental results validate the recognition performance of the proposed framework with a recognition accuracy of 80$\%$ at signal-to-noise ratio $>-18$ dB and show higher robustness on the power ratios and generative performance compared with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Deinterleaving mixtures of radar pulse sequences is the first and the most vital step for modern electronic reconnaissance systems to intercept and analyze the intentions of noncooperative radars. Currently, these systems are facing great challenges due to the emerging complexity of radar modulations. This article proposes a novel method for deinterleaving mixtures of radar pulse sequences based on the time series characteristics of each source (component pulse sequences). First, the mathematical representations are established to describe the structural characteristics of each source. Then, the deinterleaving problem is formulated as a minimization of a maximum-likelihood cost function that can be solved efficiently through a supervised neural machine translation (NMT) network, i.e., to translate each received pulse in the interleaved pulse sequence to the corresponding source label. A sequence-to-sequence NMT model is proposed to capture the structural relationships among the nonadjacent pulses originated from the same source in the mixtures and assign the corresponding label to each pulse. The proposed method does not require the exact knowledge of each component pulse sequence. The experimental results based on the time of arrival sequence of mixed pulse sequences show that the proposed method outperforms the state-of-the-art deinterleaving methods and achieves satisfactory performance under highly nonideal situations with measuring noise and lost pulse conditions. The proposed method can be directly applied to other fields involving deinterleaving problems and multivariate input conditions. [ABSTRACT FROM AUTHOR]

Low probability of intercept (LPI) radar signals have been widely used in modern radars due to the advantages of being hardly intercepted by non-cooperative receivers. Therefore, the waveform recognition of LPI radar signals has recently gained increasing attention. In this letter, an improved LPI radar waveform recognition framework with local dense connection U-net and novel loss function is proposed. Specifically, radar signals are transformed into time-frequency images (TFIs) through time-frequency analysis techniques. Then, the local dense connection U-net is proposed to reduce interference of noise and enhance TFIs features. Finally, the recognition task is implemented through a deep convolutional neural network, which is trained with a noise-robust loss function called SSR-loss. Compared with other existing LPI radar waveform recognition frameworks, our method can obtain a significant improvement in recognition accuracy under noise conditions. When signal-to-noise ratio (SNR) is as low as −10 dB, the overall probability of successful recognition on twelve kinds of typical modulated signals can reach up to 91.17%. [ABSTRACT FROM AUTHOR]

Multifunction radars (MFRs) are sophisticated sensors with significant intelligence, flexibility, and agility. It is important to analyze the system behavior and interpret the intentions of an MFR through the recognition of consecutive work modes in a pulse sequence. With the rapid development of MFRs, the agility capabilities in work mode modulations and the modulation parameters have increased, making the recognition task more challenging. Therefore, it is essential to develop new methods that are more adaptive and less depended on prior information. This article proposes a new method for the time series clustering of MFR work modes named as model-based radar time series clustering. The proposed method considers the variable modulation parameters of submode and employs three algorithms for different assumptions of progressive availability of the priors of a noncooperative MFR. The experiments with four typical pulse repetition interval modulations simulation samples validate the feasibility and the superior performance of the proposed method over the state-of-the-art pulse sequence clustering methods for recognition of MFR work modes. [ABSTRACT FROM AUTHOR]