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An improved time support estimation method for overlapping automatic dependent surveillance‐broadcast signals in low signal‐to‐noise ratio region
International Journal of Satellite Communications and Networking ( IF 1.7 ) Pub Date : 2020-11-07 , DOI: 10.1002/sat.1388
Peng Ren 1 , Jianxin Wang 1 , Peixin Zhang 1 , Da Tian 2
Affiliation  

In the satellite‐based application, due to the low signal‐to‐noise ratio (SNR) of received signals, it is highly difficult to estimate the time support of overlapping automatic dependent surveillance‐broadcast (ADS‐B) signals, resulting in the failure of many separation algorithms. Besides, it is troublesome to determine the detection threshold for unknown noise variance. In this paper, we first present three estimation methods of noise variance based on eigenvalue decomposition, which are suitable for constant and slowly varying noise variance, respectively. The proposed time support estimation is therefore of constant false alarm rate property. Moreover, according to the characteristic of ADS‐B signals, we design a matched filter and propose two estimation schemes of direct form and indirect form, which significantly improves the estimation accuracy and detection probability. Finally, in combination of the well‐known projection algorithm (PA), it is concluded that with the proposed estimation method, PA exhibits a satisfactory separation performance at low SNRs.

中文翻译:

一种改进的时间支持估计方法,用于在低信噪比区域内重叠自动相关的监视广播信号

在基于卫星的应用中,由于接收信号的信噪比(SNR)低,因此很难估计重叠的自动相关监视广播(ADS-B)信号的时间支持,从而导致许多分离算法的失败。此外,确定未知噪声方差的检测阈值很麻烦。在本文中,我们首先提出了三种基于特征值分解的噪声方差估计方法,分别适用于恒定和缓慢变化的噪声方差。因此,所提出的时间支持估计具有恒定的误报率属性。此外,根据ADS-B信号的特点,我们设计了一个匹配滤波器,并提出了直接形式和间接形式的两种估计方案,大大提高了估计的准确性和检测概率。最后,结合著名的投影算法(PA),可以得出结论,使用所提出的估计方法,PA在低信噪比下表现出令人满意的分离性能。
更新日期:2020-11-07
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