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| Content Provider | IET Digital Library |
|---|---|
| Author | Gao, Yongchan Liao, Guisheng Liu, Weijian Zuo, Lei |
| Abstract | This study addresses the problem of detecting a distributed target in interference and noise with low sample support. The target signal and interference lie in two linearly independent subspaces and the spectral property of the noise is unknown. The number of available training data is too small to form a non-singular sample covariance matrix. To overcome the difficulty, the authors resort to the Bayesian framework and design two generalised likelihood ratio tests. Both detectors can reduce training requirement by utilising the a prior knowledge of the noise covariance matrix. Numerical results show that the proposed detectors provide better detection performance than the conventional solutions in training-limited scenarios. |
| Starting Page | 752 |
| Ending Page | 758 |
| Page Count | 7 |
| ISSN | 17518784 |
| Volume Number | 11 |
| e-ISSN | 17518792 |
| Issue Number | Issue 5, May (2017) |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-rsn/11/5 |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2016.0404 |
| Journal | IET Radar, Sonar & Navigation |
| Publisher Date | 2016-11-28 |
| Access Restriction | Open |
| Rights Holder | © The Institution of Engineering and Technology |
| Subject Keyword | Bayes Method Bayesian Generalised Likelihood Ratio Tests Covariance Matrices Distributed Target Detection Electromagnetic Compatibility Interference Linear Algebra Linearly Independent SubSpace Noise Covariance Matrix Nonsingular Sample Covariance Matrix Numerical Analysis Object Detection Radar Detection Radar Equipment Signal Detection Signal Interference Spectral Property Statistics System And Application |
| Content Type | Text |
| Resource Type | Article |
| Subject | Electrical and Electronic Engineering |
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