Abstract
The backward and forward consecutive mean excision (CME/FCME) algorithms are diagnostic methods for outlier (signal) detection. Since they are computationally simple, they have applications for both narrowband signal detection in cognitive radios and interference suppression. In this paper, a theoretical performance analysis framework of the CME algorithms is presented. The analysis provides simple tests of the detectability of the signals based on their shape in the considered domain (e.g., spectrum). As a consequence, results can be used to quickly check whether the CME/FCME algorithms are usable for a given problem or not without the need to resort to time consuming computer simulations. The computer simulations for random and orthogonal frequency division multiplexing (OFDM) signals show that the presented analysis is able to predict the detectability of signals well.
Highlights
Real-world data may contain samples that differ from the majority of data
Because the FCME algorithm is a concentrated signal detection method, it can be assumed that the initial set is usually clean
The values of α1 when the signal detection is impossible via the FCME algorithm are presented in Table 2 as a function of T and β1
Summary
Real-world data may contain samples that differ from the majority of data. These observations are called outliers [1,2,3,4,5]. The CME algorithms are able to operate in any frequency range (i.e., from kHz to GHz) [13] Both the CME and FCME methods and their applications have been investigated, for example, for concentrated interference suppression both in the time and frequency domains, and for narrowband signal detection in the frequency domain both in military and civilian applications [14,15,16,17]. The LAD method uses two thresholds and is able to localize the narrowband signal samples in the frequency. The LAD method has been investigated, for example, for signal detection in the frequency domain including spectrum sensing in cognitive radios [16, 17].
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