A Cognitive Beamforming Method via Range-Doppler Map Features for Skywave Radar

Lei, Zhenshuo; Chen, Hui; Zhang, Zhaojian; Dou, Gaoqi; Wang, Yongliang

doi:10.3390/rs14122879

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…Simultaneously, the sharing of resources between detection and jamming contributes to the overall efficiency of system resource utilization. However, a critical issue persists within the integrated detection and jamming system: strong co-frequency direct wave interference degrades the detection performance [7]. Traditional radar signal processing methods are unable to address this issue effectively.…”

Section: Introductionmentioning

confidence: 99%

Co-Frequency Interference Suppression of Integrated Detection and Jamming System Based on 2D Sparse Recovery

Zhang,

Lu,

Tan

et al. 2024

Remote Sensing

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The integrated detection and jamming system employs integrated signals devoid of typical radar signal characteristics for detection and jamming. This allows for the sharing of resources such as waveform, frequency, time, and aperture, significantly enhancing the overall utilization rate of system resources. However, to achieve effective interference, the integrated waveform must overlap with the adversary radar signal within the frequency band. Consequently, the detection echoes are susceptible to the strong co-frequency direct wave generated by the adversary signals. This paper proposes a co-frequency direct wave interference suppression algorithm based on 2D generalized smoothed-l0 norm sparse recovery. The algorithm exploits a joint dictionary comprising our integrated signals and adversary signals, along with the sparsity of 2D range-Doppler maps. The direct solution of the sparse decomposition optimization problem, formulated for the entire echo matrix, enhances the target detection performance for integrated signals even in the presence of robust co-frequency direct wave interference. Furthermore, the proposed method achieves robustness to interference of varying intensities through the adaptive updating and adjustment of relevant parameters. The effectiveness of the proposed method is validated through simulation and experimental results.

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Section: Introductionmentioning

confidence: 99%

Co-Frequency Interference Suppression of Integrated Detection and Jamming System Based on 2D Sparse Recovery

Zhang,

Lu,

Tan

et al. 2024

Remote Sensing

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“…These echoes from the sea surface, i.e., sea clutter, are supposed to interfere with the observation of targets of interest. Once the sea wave becomes sufficiently wild, it will introduce the probability of a false alarm, which is almost inevitable in many kinds of radar systems [2,3]. In addition, owing to its physical property, the Doppler spectrum of sea clutter is prone to being wide.…”

Section: Introductionmentioning

confidence: 99%

Sea Clutter Amplitude Prediction via an Attention-Enhanced Seq2Seq Network

Chen²,

Lei

et al. 2023

Remote Sensing

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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.

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