天线组阵中基于SUMPLE的联合信号检测
发布时间:2018-07-28 17:19
【摘要】:针对低信噪比下天线组阵信号的检测问题,本文提出了一种基于SUMPLE合成的联合检测算法。传统信号检测算法在低信噪比下的检测效率很低,并且难以直接应用于多天线组阵系统。本算法综合利用SUMPLE合成时权值收敛特性以及各路信号之间强相关性,对信号存在性进行检测。理论分析和仿真结果表明,该算法具有适用于多天线组阵系统以及检测概率高的优势。并且在接收信号信噪比低至-12 d B时,算法仍然有80%以上的检测概率,提高了传统多天线组阵信号检测的性能。
[Abstract]:Aiming at the problem of antenna array signal detection in low SNR, a joint detection algorithm based on SUMPLE synthesis is proposed in this paper. The detection efficiency of traditional signal detection algorithm is very low in low SNR, and it is difficult to be directly applied to multiday array system. In this algorithm, the existence of SUMPLE signals is detected by using the convergence characteristics of weights and the strong correlation between different signals. Theoretical analysis and simulation results show that the algorithm is suitable for multiday array system and has high detection probability. When the signal-to-noise ratio of received signal is as low as -12 dB, the algorithm still has more than 80% detection probability, which improves the performance of traditional multi-day array signal detection.
【作者单位】: 解放军信息工程大学信息系统工程学院;
【基金】:国家自然科学基金(61501517)
【分类号】:TN820;TN911.23
本文编号:2151024
[Abstract]:Aiming at the problem of antenna array signal detection in low SNR, a joint detection algorithm based on SUMPLE synthesis is proposed in this paper. The detection efficiency of traditional signal detection algorithm is very low in low SNR, and it is difficult to be directly applied to multiday array system. In this algorithm, the existence of SUMPLE signals is detected by using the convergence characteristics of weights and the strong correlation between different signals. Theoretical analysis and simulation results show that the algorithm is suitable for multiday array system and has high detection probability. When the signal-to-noise ratio of received signal is as low as -12 dB, the algorithm still has more than 80% detection probability, which improves the performance of traditional multi-day array signal detection.
【作者单位】: 解放军信息工程大学信息系统工程学院;
【基金】:国家自然科学基金(61501517)
【分类号】:TN820;TN911.23
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