自相关加权融合的高精度频率估计算法
2024,46(4):222-228
刘康
空军预警学院 信息对抗系, 湖北 武汉 430019，416379262@qq.com
何明浩
空军预警学院 信息对抗系, 湖北 武汉 430019
陈昌孝
空军预警学院 信息对抗系, 湖北 武汉 430019
曾莎
空军预警学院 信息对抗系, 湖北 武汉 430019
李争
空军预警学院 信息对抗系, 湖北 武汉 430019
空军预警学院 信息对抗系, 湖北 武汉 430019，416379262@qq.com
何明浩
空军预警学院 信息对抗系, 湖北 武汉 430019
陈昌孝
空军预警学院 信息对抗系, 湖北 武汉 430019
曾莎
空军预警学院 信息对抗系, 湖北 武汉 430019
李争
空军预警学院 信息对抗系, 湖北 武汉 430019
摘要：
为保证雷达对抗侦察系统在复杂电磁环境下的工作性能,提出了一种基于自相关加权融合的多段信号频率估计算法。对各段含噪信号进行自相关处理得到初相位为零、频率与原信号一致的高信噪比正弦信号,利用反余切算子构建支持度矩阵对自相关信号进行实时加权融合,在粗估计基础上建立参考信号,通过最小化误差函数求得高精度频率估计结果。仿真结果表明,相较于现有算法,本文算法在满足较低计算量的同时,不但精度明显提高,且在不同信噪比、信号长度以及信号异常等条件下均具备稳定的估计性能,为基于多传感器的雷达对抗情报侦察提供了参考。
为保证雷达对抗侦察系统在复杂电磁环境下的工作性能,提出了一种基于自相关加权融合的多段信号频率估计算法。对各段含噪信号进行自相关处理得到初相位为零、频率与原信号一致的高信噪比正弦信号,利用反余切算子构建支持度矩阵对自相关信号进行实时加权融合,在粗估计基础上建立参考信号,通过最小化误差函数求得高精度频率估计结果。仿真结果表明,相较于现有算法,本文算法在满足较低计算量的同时,不但精度明显提高,且在不同信噪比、信号长度以及信号异常等条件下均具备稳定的估计性能,为基于多传感器的雷达对抗情报侦察提供了参考。
基金项目：
国家部委基金资助项目(KJ20191A050339)
国家部委基金资助项目(KJ20191A050339)
High precision frequency estimation algorithm for autocorrelation weighted fusion
LIU Kang
Department of Information Warfare, Air Force Early Warning Academy, Wuhan 430019, China,416379262@qq.com
何明浩
Department of Information Warfare, Air Force Early Warning Academy, Wuhan 430019, China
CHEN Changxiao
Department of Information Warfare, Air Force Early Warning Academy, Wuhan 430019, China
ZENG Sha
Department of Information Warfare, Air Force Early Warning Academy, Wuhan 430019, China
LI Zheng
Department of Information Warfare, Air Force Early Warning Academy, Wuhan 430019, China
Department of Information Warfare, Air Force Early Warning Academy, Wuhan 430019, China,416379262@qq.com
何明浩
Department of Information Warfare, Air Force Early Warning Academy, Wuhan 430019, China
CHEN Changxiao
Department of Information Warfare, Air Force Early Warning Academy, Wuhan 430019, China
ZENG Sha
Department of Information Warfare, Air Force Early Warning Academy, Wuhan 430019, China
LI Zheng
Department of Information Warfare, Air Force Early Warning Academy, Wuhan 430019, China
Abstract：
In order to ensure the performance of radar countermeasure reconnaissance system in complex electromagnetic environment, a multi-segment signal frequency estimation algorithm based on autocorrelation weighted fusion was proposed. Autocorrelation processing was performed on each segment of the noisy signal to obtain a high SNR sinusoidal signal with zero initial phase and the same frequency as the original signal. The arcsine operator was used to construct a support matrix to perform real-time weighted fusion of the autocorrelated signals. The reference signal was established on the basis of the rough estimation, and the high-precision frequency estimation result was obtained by minimizing the error function. The simulation results show that, compared to existing algorithms, the algorithm not only improves the accuracy significantly, but also has stable estimation performance under the conditions of different SNRs, signal lengths and signal anomalies, while satisfying the low computational cost, which provides a reference for radar countermeasures intelligence reconnaissance based on multi-sensors.
In order to ensure the performance of radar countermeasure reconnaissance system in complex electromagnetic environment, a multi-segment signal frequency estimation algorithm based on autocorrelation weighted fusion was proposed. Autocorrelation processing was performed on each segment of the noisy signal to obtain a high SNR sinusoidal signal with zero initial phase and the same frequency as the original signal. The arcsine operator was used to construct a support matrix to perform real-time weighted fusion of the autocorrelated signals. The reference signal was established on the basis of the rough estimation, and the high-precision frequency estimation result was obtained by minimizing the error function. The simulation results show that, compared to existing algorithms, the algorithm not only improves the accuracy significantly, but also has stable estimation performance under the conditions of different SNRs, signal lengths and signal anomalies, while satisfying the low computational cost, which provides a reference for radar countermeasures intelligence reconnaissance based on multi-sensors.
收稿日期：
2022-04-12
2022-04-12
