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Evaluation of a class of NLFM radar signals
EURASIP Journal on Advances in Signal Processing ( IF 1.7 ) Pub Date : 2019-12-21 , DOI: 10.1186/s13634-019-0658-9
Sebastian Alphonse , Geoffrey A. Williamson

Signal design is an important component for good performance of radar systems. Here, the problem of determining a good radar signal with the objective of minimizing autocorrelation sidelobes is addressed, and the first comprehensive comparison of a range of signals proposed in the literature is conducted. The search is restricted to a set of nonlinear, frequency-modulated signals whose frequency function is monotonically nondecreasing and antisymmetric about the temporal midpoint. This set includes many signals designed for smaller sidelobes including our proposed odd polynomial frequency signal (OPFS) model and antisymmetric time exponentiated frequency modulated (ATEFM) signal model. The signal design is optimized based on autocorrelation sidelobe levels with constraints on the autocorrelation mainlobe width and leakage of energy outside the allowed bandwidth, and we compare our optimized design with the best signal found from parameterized signal model classes in the literature. The quality of the overall best such signal is assessed through comparison to performance of a large number of randomly generated signals from within the search space. From this analysis, it is found that the OPFS model proposed in this paper outperforms all other contenders for most combinations of the objective functions and is expected to be better than nearly all signals within the entire search set.



中文翻译:

评估一类NLFM雷达信号

信号设计是雷达系统良好性能的重要组成部分。在此,解决了以最小化自相关旁瓣为目标的确定良好雷达信号的问题,并对文献中提出的信号范围进行了首次全面比较。搜索限于一组非线性的调频信号,其频率函数单调非递减且关于时间中点反对称。这组信号包括许多为较小旁瓣设计的信号,包括我们提出的奇多项式频率信号(OPFS)模型和反对称时间指数频率调制(ATEFM)信号模型。基于自相关旁瓣电平,自相关主瓣宽度和允许带宽外的能量泄漏的约束条件,对信号设计进行了优化,并将我们的优化设计与从文献中的参数化信号模型类中找到的最佳信号进行了比较。通过与来自搜索空间内的大量随机生成的信号的性能进行比较,可以评估总体上最好的此类信号的质量。从该分析中发现,对于大多数目标函数组合,本文提出的OPFS模型优于所有其他竞争者,并且有望比整个搜索集中的几乎所有信号都要好。通过与来自搜索空间内的大量随机生成的信号的性能进行比较,可以评估总体上最好的此类信号的质量。从该分析中发现,对于大多数目标函数组合,本文提出的OPFS模型优于所有其他竞争者,并且有望比整个搜索集中的几乎所有信号都要好。通过与来自搜索空间内的大量随机生成的信号的性能进行比较,可以评估总体上最好的此类信号的质量。从该分析中发现,对于大多数目标函数组合,本文提出的OPFS模型优于所有其他竞争者,并且有望比整个搜索集中的几乎所有信号都要好。

更新日期:2020-04-21
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