Radio Frequency Interference Detection in Passive Microwave Remote Sensing Using One-Class Support Vector Machines
Radio Frequency Interference Detection in Passive Microwave Remote Sensing Using One-Class Support Vector Machines
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DOI：
10.1109/jstars.2023.3293393
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发表时间：
2023
影响因子：
5.5
通讯作者：
I. Nazar;M. Aksoy
中科院分区：
文献类型：
--
作者：
I. Nazar;M. Aksoy
射频干扰（RFI）是一个严重的威胁，通过被动微波遥感的关键地球物理参数的准确估计和RFI的微波辐射计测量中的存在是随着时间的推移越来越多。另一方面，辐射计捕捉到的射频干扰的性质和发生情况通常是未知的，因此难以探测和减缓。为了克服这一挑战，本文提出了一种新的RFI检测算法，该算法仅依赖于从无RFI辐射计测量中提取的信息，这些测量可以在人类活动有限的海洋和农村地区收集，即，一类算法，将在未来的遥感辐射计实施。该算法将原始的时间序列辐射计测量到一个异构的基于特征的表示。然后，一个特征选择算法确定最有鉴别力的功能，以检测基于误检和误报的概率的干扰。最后，通过支持向量机（SVM）计算的最佳决策边界，区分射频干扰污染的辐射计测量从射频干扰免费的测量。因此，无论RFI污染的特性如何，该算法都为无RFI测量输出广义决策边界。针对传统的RFI检测算法的性能评估已进行使用模拟辐射计数据，结果表明，新的算法，与传统的方法不同，可以成功地检测RFI，即使当辐射计测量的干扰噪声比（INR）低至$-18$ dB。
Radio frequency interference (RFI) is a serious threat to the accurate estimation of critical geophysical parameters via passive microwave remote sensing and the presence of RFI in microwave radiometer measurements is increasing over time. On the other hand, the nature and the occurrence of RFI captured by radiometers are usually unknown making their detection and mitigation difficult. To overcome this challenge, this article presents a novel RFI detection algorithm that relies only on the information extracted from the RFI-free radiometer measurements which can be collected over oceans and rural areas with limited human activity, i.e., a one-class algorithm, to be implemented in future remote sensing radiometers. The algorithm transforms raw time-series radiometer measurements into a heterogeneous feature-based representation. Then, a feature selection algorithm identifies the most discriminant features to detect interference based on the probabilities of misdetections and false alarms. Finally, the optimal decision boundaries that discriminate the RFI-contaminated radiometer measurements from the RFI-free ones are computed via support vector machines (SVM) using only the RFI-free radiometer measurements. Regardless of the characteristics of RFI contamination, the algorithm, therefore, outputs a generalized decision boundary for RFI-free measurements. A performance evaluation of the proposed algorithm against the traditional RFI detection algorithms has been performed using simulated radiometer data, and the results have shown that the novel algorithm, unlike the traditional methods, can successfully detect RFI, even when the interference-to-noise ratio (INR) of the radiometer measurements is as low as $-18$ dB.