The increasingly complex radio environment may cause the received low probability of intercept (LPI) radar signals to overlap in time–frequency domains. Analyzing overlapping LPI radar signals requires identifying the modulation type and estimating the parameters of each component. Prior research performs overlapping signal analysis as a multistage task, where each stage is designed to perform a part of the task. The multistage system will increase the calculation burden and cannot be optimized as a whole. Instead, this article proposes a novel framework for analyzing overlapping signals in a single stage. Specifically, we develop a joint semantic learning deep convolutional neural network (JSLCNN) that jointly learns three tasks, i.e., feature restoration, modulation classification, and parameter regression. Since the whole cognitive pipeline is a single network, it can be optimized end-to-end directly on cognitive performance. To verify the validity of the proposed JSLCNN, numerous comparative experiments are carried out in terms of modulation recognition and parameter estimation of overlapping signals. Experimental results demonstrate that the JSLCNN has desirable extensibility for identifying unseen signal combinations and robustness against unknown jamming. Furthermore, we show that the JSLCNN outperforms other existing approaches in generic real-time parameter estimation for LPI radar signals.

The recognition and estimation of frequency-modulated continuous-wave (FMCW) radar signals is critical for both military electronic countermeasures (ECM) and civilian autonomous driving. However, the increasingly complex radio environment poses two new challenges for detection equipment. First, multiple FMCW radars can share the same frequency band, resulting in overlap of received FMCW signals in the time-frequency domain. Second, unexpected signals in unknown spectrum environments can affect the cognitive performance of FMCW signals. This paper proposes a semantic-based learning network (SLN) that simultaneously learns modulation classification and parameter regression of FMCW signals. By integrating recognition and estimation into a single network, the system can be optimized end-to-end as a whole. Additionally, instance-level semantic learning facilitates the parallel analysis of multiple components in overlapping signals. Finally, contrastive clustering in SLN achieves suppression of unexpected signals. Numerous comparative experimental results demonstrate that SLN has the desirable ability to simultaneously recognize and estimate FMCW signals in real-time, even in unknown spectrum environments.

The emergence of active jamming technology has led to a surge of interest in jamming suppression. This work proposes a one-dimensional adaptive multi-scale residual autoencoder (1-DAMRAE) to take overlapping signals (echo signals and jamming signals) as input and restore correspondingly-sized echo signals, which frames the jamming suppression as a regression problem. 1-DAMRAE incorporates three key components. First, adaptive multi-scale convolutional blocks are developed to adaptively fuse multi-scale information for a more robust representation learning. Second, a fully residual design is explored to improve information flow within and across blocks. Third, given the unknown of jamming sources, our method encodes contextual dependencies and produces a plausible hypothesis for the potential missing information to handle out-of-distribution (OOD) jamming. To verify the reliability of 1-DAMRAE, the dynamic short-range detection system model is established. Experimental results show that 1-DAMRAE achieves excellent anti-jamming performance while keeping complexity and computation cost at a low level. Even facing unseen OOD jamming, 1-DAMRAE can still achieve desirable anti-jamming performance. The model and data are available in https://github.com/Drchenjust/1-DAMRAE .

The continuous advancement of shipbuilding technology has enabled the further development of sea transport and naval vessels, which also poses significant challenges to navigation safety. Accurate recognition of different types of ships is crucial. In this study, three typical ships, namely a destroyer, cruise ship, and aircraft carrier, were taken as examples. The micro-Doppler effect of these ships was analyzed using a unmanned aerial vehicle (UAV)-based ultrawideband (UWB) radar. Echo signal models were established and compared with measured Doppler signals. Furthermore, a lightweight self-supervised learning network was proposed to achieve accurate recognition of small-sample labeled ship targets. The neural network architecture, including specific layers and parameters, was introduced. To enhance the micro-Doppler features, the 1-D signals were transformed into 2-D images, thereby converting the ship recognition problem into an image classification problem. The experimental results demonstrate that the proposed network achieves high recognition accuracy under different situations. When evaluated on measured data, the overall recognition rate reaches 97.50%. Moreover, even in challenging scenarios with a low signal-to-clutter ratio (SCR) of −10 dB, the overall recognition rate for simulated data still exceeds 90%, demonstrating the effectiveness and advantages of the proposed network.

With the rapid development of electronic technology, the electromagnetic interference encountered by airborne synthetic aperture radar (SAR) is no longer satisfied with a single type of interference, and it often encounters both suppressive and deceptive interference. In this manuscript, an algorithm based on blind signal separation (BSS) and deep residual learning is proposed for airborne SAR multi-electromagnetic interference suppression. Firstly, theoretical airborne SAR imaging in a multi-electromagnetic interference environment model is established, and the signal-mixed model of multi-electromagnetic interference is proposed. Then, a BSS algorithm using maximum kurtosis deconvolution and improved principal component analysis (PCA) is presented for suppressing the composite electromagnetic interference encountered by airborne SAR. Finally, in order to find the desired signal among multiple separated sources and to cope with the residual noise, a deep residual network is designed for signal recognition and denoising. This method uses a BSS algorithm with maximum kurtosis deconvolution and improved PCA to perform mixed signal separation. After performing signal separation, the original echo signal and the jamming can be obtained. To solve the separation order uncertainty and residual noise problems of the existing BSS algorithms, the deep residual network is designed to recognize airborne SAR signals after airborne SAR imaging. This algorithm has a better signal restoration degree, higher image restoration degree, and better compound interference suppression performance before and after anti-interference. Simulation and measurement results demonstrate the effectiveness of our presented algorithm.