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首页> 外文期刊>Applied Acoustics >A recognition method for time-frequency overlapped waveform-agile radar signals based on matrix transformation and multi-scale center point detection
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A recognition method for time-frequency overlapped waveform-agile radar signals based on matrix transformation and multi-scale center point detection

机译：基于矩阵变换的时频重叠波形雷达信号的识别方法和多尺度中心点检测

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摘要

The new digital programmable radar systems allow the development of waveform agility technique that can be reconfigured to work with different kinds of waveforms without the introduction of any hardware modification, and it also extends to the combination of the carrier frequency agility, intra-pulse chirp, and pulse repetition frequency jittering. The challenging recognition problem of waveform-agile radar signals results in the difficult implementation of the prior electronic countermeasures and interferences. Meanwhile, the common problem under wideband reception is that multiple overlaps easily occur in the time and frequency domain among sources. In this paper, we take a fresh look at this problem, a method based on matrix transformation is proposed from the perspective of the source independence, to extract the waveform-agile radar sources in the underdetermined receiving scenario. Next, a multiscale center point detection method is designed to locate and identify the internal waveforms to recognize the whole signal, and an additional box selecting algorithm is proposed to reduce the computing burden. Simulations demonstrate that the developed technique effectively solves the source attribution problem, and also outperforms the traditional methods in recognition performance under low signal-to-noise ratio (SNR). (C) 2020 Elsevier Ltd. All rights reserved.
机译：新的数字可编程雷达系统允许开发波形敏捷技术，可以重新配置以使用不同类型的波形，而不会引入任何硬件修改，并且它还扩展到载波频率敏捷，脉冲内啁啾的组合，和脉冲重复频率抖动。波形敏捷雷达信号的具有挑战性的识别问题导致难以实现先前的电子对策和干扰。同时，宽带接收下的常见问题是在源之间的时间和频域中容易发生多个重叠。在本文中，我们采取了新的问题，从源独立性的角度提出了一种基于矩阵变换的方法，从而提取未经确定的接收方案中的波形敏捷雷达源。接下来，设计多尺度中心点检测方法来定位和识别识别整个信号的内部波形，并提出了一个额外的盒子选择算法以减少计算负担。仿真表明，开发技术有效解决了源归因问题，并且在低信噪比（SNR）下识别性能中的传统方法效果优异。 （c）2020 elestvier有限公司保留所有权利。

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