Link16信号联合优化跳周期估计方法研究
Transactions of Shenyang Ligong University（2022）
Abstract
针对Link16信号参数估计精度低等问题,提出一种重排谱图和聚类算法K-means联合的Link16信号跳周期估计方法,该方法首先利用重排谱图时频分析获得Link16信号清晰的时频图,然后通过K-means聚类算法获得相邻两跳的最终迭代聚类中心,最后对其相邻两跳的聚类中心做差分运算,并对所得差值进行算术平均,得到Link16信号的跳周期.仿真结果表明,重排谱图比谱图、维格分布(WVD)具有更好的跳周期估计性能,在信噪比高于-2 dB时能够得到较精确的估计结果.
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