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| Content Provider | IET Digital Library |
|---|---|
| Author | Sha, Zhi Chao Huang, Zhi Tao Zhou, Yi Yu Wang, Feng Hua |
| Abstract | Most of the earlier approaches for frequency-hopping (FH) signals sorting with a single sensor do not adapt to synchronous network, whereas the multiple-sensor-based algorithms request the number of sensors must be more than that of signals. Since the number of sensors is limited in many practical applications, it is important to sort synchronous or asynchronous FH networks with as little as possible sensors. This study introduces the underdetermined blind source separation (UBSS) algorithm to solve this problem. Considering the time–frequency (TF) sparsity of FH signals, the problem is formulated as one of UBSS based on sparse TF representation. First, an improved k-means clustering algorithm is developed to estimate the mixing matrix, according to the TF properties of FH signals. Secondly, to separate more signals overlapped in the TF domain using given sensors, an improved subspace-based algorithm utilising the information of the comparative power is proposed. In the proposed method, the sparsity condition of the sources in the TF domain is relaxed, and the number of FH signals that exist at any TF point simultaneously is allowed to equal that of the sensors. Simulations demonstrate that the proposed method can separate FH signals efficiently and outperforms the previous methods. |
| Starting Page | 1456 |
| Ending Page | 1464 |
| Page Count | 9 |
| ISSN | 17518628 |
| Volume Number | 7 |
| e-ISSN | 17518636 |
| Issue Number | Issue 14, Sep (2013) |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-com/7/14 |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-com.2013.0276 |
| Journal | IET Communications |
| Access Restriction | Open |
| Rights Holder | © The Institution of Engineering and Technology |
| Subject Keyword | Blind Source Separation Comparative Power Estimation Theory Frequency Hop Communication Frequency Hopping Signal K-means Clustering Algorithm Mixing Matrix Estimation Multiple Access Communication Radio Link And Equipment Signal Processing And Detection Source Sparsity Condition Sparse Time-frequency Representation Statistics Time-frequency Domain Time-frequency Point Time-frequency Sparsity UBSS Algorithm Underdetermined Blind Source Separation |
| Content Type | Text |
| Resource Type | Article |
| Subject | Electrical and Electronic Engineering Computer Science Applications |
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