认知MCPC雷达模糊函数的形成

2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) Pub Date : 2017-11-01 DOI:10.1109/ISPACS.2017.8266509

Jing Tan, Jingqi Wang, Yurou Tian, Wei Xue, Wen Wu

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引用次数: 3

摘要

模糊函数(AF)是衡量雷达系统探测能力的关键参数。认知雷达提供了干扰的反馈信息，从而创造了理想的雷达AF重构的可能性。提出了一种认知型多载波相位编码(MCPC)雷达的AF整形方法，根据预测信息对发射的MCPC信号进行调整。最优准则是在能量约束下最大信噪比，该问题具有复杂的四次约束和非凸约束，属于np困难问题。首先根据信号的特点，通过推导过程对模型进行简化。然后，通过最大化最小化算法对目标函数进行优化。最后，数值结果表明，不希望的距离-多普勒本和自相关副瓣的水平降低。设计的信号提高了雷达的探测能力和对环境的适应能力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Ambiguity function shaping for cognitive MCPC radar

The ambiguity function (AF) plays a key role in radar systems to measure their detection ability. Cognitive radars provide feedback information of interference, and therefore create the possibility of reshaping AF of radars ideally. In this paper, an AF shaping method for a cognitive multicarrier phase coded (MCPC) radar, which adjusts the transmitted MCPC signal according to predicted information, is presented. The optimality criterion is maximizing the signal to interference and noise ratio under an energy constraint, which is an NP-hard problem due to its complex quartic and nonconvex constraint. The model is firstly simplified according to the signal characteristics by a process of deduction. Then, the objective function is optimized through a majorization minimization algorithm. Finally, numerical results show a level reduction of the undesired range-Doppler bins and autocorrelation sidelobes. The designed signal improves the radar's probing and adapting to the environment.

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2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)

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