自适应估计信号个数的频谱信号提取算法

无线电监测频谱数据中包含有大量的信号，准确提取这些信号有利于掌握全频段的频谱使用情况。实际信号的频谱由于受噪声干扰，会出现信号频带内个别频点能量值低于检测门限的情况，此时传统的门限检测算法会将该信号错估为多个信号，产生多个虚假的相邻信号间隔，导致频谱信号提取的准确率下降。对此，根据虚假相邻信号间隔特点，提出一种自适应估计信号个数的频谱信号提取算法，该算法可自动、准确估计频谱监测数据中电磁信号的个数，并将对应的信号及频谱信息提取出来。实验结果表明，该方法具有自适应性、强鲁棒性和高准确性，有效提高了频谱信号提取的准确率，为军、民电磁环境的识别与掌握提供基础的电磁信号数据支撑。

Spectrum extraction algorithm for adaptive estimation of the signal number

Spectrum data contains a large number of signals in radio monitoring. Accurate extraction of these signals is conducive to mastering the spectrum usage of the whole frequency band. Due to the interference of noise, several energy values of frequency points in the signal spectrum bandwidth will be lower than the detection threshold, then the traditional threshold detection algorithm will misestimate the signal as multiple signals and generate multiple false adjacent signal intervals, resulting in a decline of the spectrum signal extraction accuracy. To tackle this problem, an algorithm of spectrum signal extraction for estimating the signal number adaptively is proposed according to the characteristics of false adjacent signal intervals. The new method can estimate the number of electromagnetic signals in the spectrum monitoring data, and extract the corresponding signal and spectrum information accurately and automatically. The experimental result shows that the new method is adaptive, strongly robust and accurate, effectively improves the accuracy of spectrum signal extraction. It can provide basic electromagnetic signal data for supporting the identification of electromagnetic environment in military and civil spectrum monitoring.