Abstract
In the processing of acquiring the observed data by using sensors to collect sources, it is very important to estimate the number of sources for signal processing and observed data analysis. In order to determine the number of sparse sources, this paper proposes a visual estimation method to enhance the linear clustering characteristics of signals. Firstly, the short time Fourier transform (STFT) is used to transform the observed signal in the time domain into a complex spectrum in the frequency domain to enhance the sparsity of the observed data. Then, a similarity measure of angle cosine is established, and the angle threshold between the real part and imaginary part of the spectrum is used to determine the source of the data points. Finally, the angle threshold is applied to single-source-point (SSP) detection to eliminate the multiple-source-point (MSP) that causes interference and highlights the linear clustering characteristics of sparse sources. The experimental results show that the proposed method can effectively enhance the linear clustering characteristics of the observed data and realize the intuitive estimate the number of sources.
| Translated title of the contribution | A visual linear clustering method for estimating the number of sources |
|---|---|
| Original language | Chinese (Traditional) |
| Pages (from-to) | 1261-1268 |
| Number of pages | 8 |
| Journal | Gaojishu Tongxin/High Technology Letters |
| Volume | 31 |
| Issue number | 12 |
| DOIs | |
| Publication status | Published - 25 Dec 2021 |