Marine target detection under diverse sea conditions has been a research hotspot. An effective sea clutter suppression method is supposed to be beneficial to marine target detection. However, traditional model-driven suppression methods are likely to suffer from the model mismatch problem. To overcome these problems, we propose a data-driven sea clutter suppression method based on an image-to-image neural network. The proposed method first takes complex-valued range-Doppler (RD) images as inputs by concatenating real and imaginary parts. The designed network is then capable of capturing significant phase information and learning deep features of both targets and clutter. Finally, clutter-free RD images are predicted directly by the proposed network. Meanwhile, a consistency loss is elaborately designed to suppress clutter residuals and noise points with high energy. Measured results with the Sea-Detecting Radar Data-sharing Program (SDRDSP) database verify the advantages of the consistency loss and show that the proposed network outperforms its real-valued counterpart. Compared with four classical clutter suppression methods, the proposed method achieves the best suppression performance with relatively lower computational complexity.

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.

Self-defense smart noise jamming can automatically aim a signal frequency and obtain antenna gain and matching filtering processing gain, posing a huge threat to the normal performance of radar. In response to this situation, this article conducts an in-depth analysis of two typical smart noise jamming methods: noise convolution jamming methods and noise product jamming methods. The distribution characteristics of the two jamming methods in both time–frequency dimensions and their internal time–frequency relationships are analyzed. Based on this, a self-defense smart noise jamming suppression method based on pulse frequency stepping is proposed. This method obtains the true distance information of the target based on the phase difference caused by frequency stepping between adjacent pulses and uses this information to construct a filter to filter the radar echo, achieving jamming suppression. Simulation experiments have verified the effectiveness of this method.

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.

Compressive sensing technique has been widely applied to achieve range-Doppler reconstruction of high frequency radar by utilizing sparse random stepped-frequency (SRSF) signal, which can suppress the complex electromagnetic interference and greatly reduce the coherent processing interval. An important way to improve the performance of sparse signal reconstruction is to optimize the sensing matrix (SM). However, the existing research on the SM optimization needs to design a measurement matrix with superior performance, which needs a large amount of computation and does not consider the influence of the waveform parameters design. In order to improve the superior reconstruction performance, a novel SM optimization approach for SRSF signal is proposed by using two-dimensional ambiguity function (TDAF) in this paper. Firstly, based on the two-dimensional sparse reconstruction model of the SRSFs, the internal relationship between the waveform parameters and the SM was derived. Secondly, the SM optimization problem was directly transformed into the waveform design of SRSFs. Furthermore, on the basis of analyzing the relationship between the mutual coherence matrix of SM and the TDAF matrix of SRSFs, the purpose of optimizing the SM can be achieved by designing the TDAF of the SRSFs. Based on this analysis, a sparse waveform optimization method with joint constraints of maximum and mean sidelobes of the TDAF by using the genetic algorithm was derived. Compared with the traditional SM optimization method, our method not only avoids generating a new measurement matrix, but also further reduces the complexity of the waveform optimization. Simulation experiments verified the effectiveness of the proposed method.

Stepped-frequency radar can increase the degree of freedom of the range dimension by adding a tiny stepping frequency between neighboring pulse carrier frequencies, which has a clear advantage in countering range false target jamming. However, when the jamming is released by a self-defense jammer carried by the target, the range information is coupled to the Doppler frequency. This makes it impossible for a stepped-frequency radar to extract the range information accurately. In this paper, we derive the correlation between the phase difference of adjacent pulses and range information and the Doppler frequency when the frequency is uniformly stepped, as well as the error caused by the Doppler frequency in range estimation. Then, we propose a decoupling method based on a waveform design and the corresponding suppression method of range false target jamming. Simulation results show that the proposed method can effectively suppress the jamming of self-defense range false targets.