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低信噪比场景下Link-16系统的联合频偏估计算法 被引量:1

Joint Frequency Offset Estimation for Link-16 System in Low Signal-Noise Ratio Scene
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摘要 对于Link-16数据链终端平台在低信噪比(SNR)高动态场景下的多普勒频率偏移问题,该文设计一种新的数据结构,推导了该结构下的克拉默-拉奥下界(CRLB),并在此基础上提出一种联合频域变换与时域自相关运算的分步式频偏估计算法。其基本思想是,首先对接收信号做自相关处理,然后通过频域变换进行最大值索引,结合修正因子得到多普勒频移的粗估计值,再利用时域的改进L&R算法对接收信号进行细估计,根据两步估计算法得到最终的频偏估计值。算法应用蒙特卡罗实验仿真,仿真结果表明,与传统频偏估计算法相比,该算法的归一化均方误差更接近CRLB,在多普勒频偏为[–20 kHz,20 kHz]时,估计精度可达10^(–5)。在低信噪比环境下,所提算法能达到较为理想的估计效果,适用于Link-16数据链通信。 For the Doppler frequency offset of Link-16 data link terminal platform in low Signal-Noise Ratio(SNR)and high-speed moving scene,a new data structure is designed,and a step-by-step frequency offset estimation algorithm combining frequency domain transform,and the Cramer-Rao Low Bound(CRLB)and time-domain autocorrelation is proposed on this basis.First according to the basic idea,the received signal is autocorrelated,and then the maximum index is found through frequency domain transformation.Combined with the correlation factor,the coarse estimation value of frequency offset is obtained.Then,the received signal is finely estimated by using improved L&R algorithm in time domain,and the final frequency offset estimation value is obtained according to the two-step estimation algorithm.The algorithm is simulated by Monte Carlo experiment.The simulation results show that compared with the traditional frequency offset estimation algorithm,the normalized mean square error of the algorithm is closer to CRLB,and when the Doppler frequency offset is[–20 kHz,20 kHz],the estimation accuracy can reach 10^(–5).In low SNR environment,the algorithm can achieve ideal estimation effect,which is suitable for Link-16 data link communication.
作者 宁晓燕 罗海玲 孙志国 刁鸣 NING Xiaoyan;LUO Hailing;SUN Zhiguo;DIAO Ming(College of Information and Communication Engineering,Harbin Engineering University,Harbin 150001,China)
出处 《电子与信息学报》 EI CSCD 北大核心 2023年第10期3587-3593,共7页 Journal of Electronics & Information Technology
关键词 Link-16数据链 克拉默-拉奥下界 自相关处理 改进L&R算法 分步式估计算法 Link-16 data link Cramer-Rao Low Bound(CRLB) Autocorrelation Improved L&R algorithm Distributed estimation algorithm
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