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Since solving the path of radio wave propagation by an analytic method in the process of coordinate registration (CR) for skywave over‐the‐horizon radar (OTHR) is difficult, a reference source‐aided CR method that uses ensemble learning is proposed. First, the OTHR wave propagation principle is briefly described, and the ionospheric channel characteristics implied by reference sources and target information are explained, which provides a theoretical basis for modelling. Then, multiple machine learning models, such as random forest, support vector machine and elastic network, are used to build a mapping network between known information and the actual ground distance of the targets. Moreover, the prediction ability and correlation of each machine learning model are evaluated. Finally, with stacking ensemble learning, machine learning models with better performance to solve CR problems are effectively combined as base learners. The models develop the advantages of each base learner to achieve a perception of the ionospheric environment and accurate OTHR CR. The simulation results show that the proposed method can mine the ionospheric channel propagation characteristics and effectively reduce the CR error of the targets of interest. When the information provided by 4 reference sources 200 km away from the target is used, the average error of target positioning is 13 km. [ABSTRACT FROM AUTHOR]

For skywave over-the-horizon radar, beamforming techniques are often used to suppress airspace radio frequency interference because the high-frequency band is shared by many devices. To address the problems that the traditional beamforming method is not capable of recognizing the electromagnetic environment and that its performance is greatly affected by the accuracy of signal feature estimation, a cognitive beamforming method using range-Doppler (RD) map features for skywave radar is proposed. First, the RD map is weighted by a local attention model, and then, texture features are extracted as the inputs to a support vector machine. Finally, the support vector machine is used to predict the optimal diagonal loading factor. Simulation results show that the output signal-to-interference-plus-noise ratio is improved compared with previous methods. The proposed method is suitable for many kinds of common unsatisfactory scenarios, making it beneficial for engineering implementation. [ABSTRACT FROM AUTHOR]

Jamming recognition is a significant prior step to achieving effective jamming suppression, and the precise results of the jamming recognition will be beneficial to anti-jamming decisions. However, as the electromagnetic environment becomes more complex, the received signals may contain both suppression jamming and deception jamming, which is more challenging for existing methods focused on a single kind of jamming. In this paper, a recognition method for compound jamming based on a dual-channel neural network and feature fusion is proposed. First, feature images of compound jamming are extracted by the short-time Fourier transform and the wavelet transform. Feature images are then employed as inputs for the proposed network. During parallel processing in dual-channel, the proposed network can adaptively extract and learn task-relevant features via the attention modules. Finally, the output features in dual-channel are fused in the fusion subnetwork. Compared with existing methods, the proposed method can yield better recognition performance with less inference time. Additionally, compared with existing fusion strategies, the fusion subnetwork can further improve the recognition performance under low jamming-to-noise ratio conditions. Results with the semi-measured datasets also verify the feasibility and generalization performance of the proposed method. [ABSTRACT FROM AUTHOR]

Sea clutter is a kind of ubiquitous interference in sea-detecting radars, which will definitely influence target detection. An accurate sea clutter prediction method is supposed to be beneficial while existing prediction methods are based on the one-step-ahead prediction. In this paper, a sea clutter prediction network (SCPNet) is proposed to achieve the k-step-ahead prediction based on the characteristics of sea clutter. The SCPNet takes a sequence-to-sequence (Seq2Seq) structure as the backbone, and a simple self-attention module is employed to enhance the ability of adaptive feature selections. The SCPNet takes the normalized amplitudes of sea clutter as inputs and is capable of predicting an output sequence with a length of k; the phase space reconstruction theory is also used to find the optimized input length of the sea clutter sequence. Results with the sea-detecting radar data-sharing program (SDRDSP) database show the mean square error of the proposed method is 1.48 × 10−5 and 8.76 × 10−3 in the one-step-ahead prediction and the eight-step-ahead prediction, respectively. Compared with four existing methods, the proposed method achieves the best prediction performance. [ABSTRACT FROM AUTHOR]

Low-frequency bands are an important way to realize stealth target detection for airborne radars. However, in a complex electromagnetic environment; when low-frequency airborne radar operates over land, it will inevitably encounter a lot of unintentional communication and intentional interference, while effective suppression of interference can not be achieved only through the adaptive processing of the receiver. To solve this problem, this paper proposes optimizing an algorithm designed for sparse-frequency waveforms for use in airborne radars. The algorithm establishes a joint objective function based on the criteria of minimizing waveform energy in the spectrum stopband and minimizing the integrated sidelobe level of specified range cells. The waveform is optimized by a cyclic iterative algorithm based on the Fast Fourier Transform (FFT) operation. It can ensure the frequency domain stopband constraint to realize the effective suppression of main-lobe interference while forming lower-range sidelobes at specified range cells to improve the ability to detect dim targets. Theoretical analysis and simulation results have shown that the algorithm has good anti-interference performance. [ABSTRACT FROM AUTHOR]