Improvement of Source Number Estimation Method for Single Channel Signal

Abstract

Source number estimation methods for single channel signal have been investigated and the improvements for each method are suggested in this work. Firstly, the single channel data is converted to multi-channel form by delay process. Then, algorithms used in the array signal processing, such as Gerschgorin’s disk estimation (GDE) and minimum description length (MDL), are introduced to estimate the source number of the received signal. The previous results have shown that the MDL based on information theoretic criteria (ITC) obtains a superior performance than GDE at low SNR. However it has no ability to handle the signals containing colored noise. On the contrary, the GDE method can eliminate the influence of colored noise. Nevertheless, its performance at low SNR is not satisfactory. In order to solve these problems and contradictions, the work makes remarkable improvements on these two methods on account of the above consideration. A diagonal loading technique is employed to ameliorate the MDL method and a jackknife technique is referenced to optimize the data covariance matrix in order to improve the performance of the GDE method. The results of simulation have illustrated that the performance of original methods have been promoted largely.

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

“…All the eigenvalues in the different SNR are shown in Table. 3. According to (23) or (24), the source number can be calculated. It finds that both the AIC and the MDL are capable to correctly detect the three sources in the cases of SNR = 5dB, SNR = 10dB and SNR = 15dB.…”

“…However, it is the difficulty to choose an appropriate wavelet basis for performing the decomposition. In [24], the data of the single channel is converted into that of the multiple channels through a delay processing. The algorithms based on the information criterion are employed to estimate the number of signal sources subsequently.…”

“…For the sake of its stronger robustness, it is widely applied in a lot of fields [32]- [34]. The method of resampling, which is also called jackknifing, is utilized to improve the source number estimation performance in the case of low SNR [24], [33]. However, the drawback of the method in [24] needs a great amount of snapshots to perform the time delay processing before jackknifing.…”

“…The method of resampling, which is also called jackknifing, is utilized to improve the source number estimation performance in the case of low SNR [24], [33]. However, the drawback of the method in [24] needs a great amount of snapshots to perform the time delay processing before jackknifing. By the way, the delay processing is important to dimension extension for the single channel signal.…”

“…In this paper, an effective sources enumeration approach is presented. The new algorithm combines with the merits of EMD, jackknifing and ACCM, such as the self-adaptivity of EMD [26], to achieve the stronger robustness in jackknifing leading to the improvement on the source number estimation performance at low SNR [24], [33] and the greater discrimination between the signal eigenvalues and the noise eigenvalues from the ACCM [35]. The observed signal received by a single channel is decomposed into a series of IMF components and a residual component by EMD.…”