An Effective Sources Enumeration Approach for Single Channel Signal at Low SNR

Abstract

In order to address the problem of achieving the poor performance of a single-channel receiver operated at a low signal-to-noise ratio (SNR) based on the existing source number estimation method, an effective source enumeration approach for the single-channel receiver operated at a low SNR is proposed in this paper. The proposed method is based on the empirical mode decomposition (EMD) with the autocorrelation coefficient matrix (ACCM) and the jackknifing method. First, the received single-channel signal is decomposed into several intrinsic mode functions and a residue via the EMD. Both the components and the original signal are employed to simulate the signals obtained from a pseudo-uniform linear array (ULA). After applying the multiple jackknifing methods to the signals obtained from the pseudo-ULA without complete overlap, we acquire a series of subsample datasets. Consequently, the ACCM instead of the conventional auto-covariance matrix is constructed for each subsample. After performing the eigenvalue decomposition of all the ACCMs, we acquire several sets of eigenvalues. For each set of eigenvalues, either moving increment criterion or moving standard deviation criterion is employed to determine the source number. Since more than one ACCM is employed because of using the multiple jackknifing methods, we have the statistics for the estimation of each ACCM. Then, we take the one that occurs the most frequently as the final result of source enumeration. The experiments validate the proposed method and show the excellent performance of our proposed algorithm. Finally, we also present the optimal scheme for selecting the important parameters, such as the jackknifing ratio and the jackknifing times in the approach. INDEX TERMS Source enumeration for single channel receiver, empirical mode decomposition (EMD), jackknifing, auto correlation coefficient matrix, moving increment criterion, moving standard deviation criterion.

An effective source number detection method for single-channel signals based on signal reconstruction and deep learning at low SNR

“…To assess the effectiveness of the proposed SND method in this paper, we compare it with existing methods, namely CNNSNE [19], ECNet [35], EJAMSTDC [36] and SSM [8]. The average recognition rates of the five detection methods are compared within the SNR range of [−20∼ 10 dB].…”

Source Enumeration Approaches Using Eigenvalue Gaps and Machine Learning Based Threshold for Direction-of-Arrival Estimation

“…Figure 7 shows the estimation accuracy of AIC, MDL, SORTE, and our two proposed approaches (AREG, T-GANE) versus SNR. The performances are evaluated in the SNR range from −20 dB to 10 dB, which is roughly chosen in many other papers [4,[10][11][12]14,16,[23][24][25]31]. This paper is interested in improving accuracy of AIC at high SNR-where MDL has 100% accuracy, but AIC does not reach 100% accuracy-and it of MDL at low SNR-where the MDL accuracy begins to decrease sharply, but AIC maintais good accuracy.…”

“…Direction-of-arrival (DOA) is a key process of ES for locating the signal sources of the enemies [2,3]. DOA is used not only for EW applications but also in many applications such as radar, sonar, wireless communication, radio astronomy, and satellite communications [4].…”