Adaptive GLRT-, Rao- and Wald-Based CFAR Detectors for Distributed Targets
| Autor: | Zuozhen Wang, Zhiqin Zhao, Juexin Zhang |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Noise power
Covariance matrix Computer science Monte Carlo method Detector 020206 networking & telecommunications 02 engineering and technology Wald test Constant false alarm rate symbols.namesake Noise Gaussian noise Likelihood-ratio test 0202 electrical engineering electronic engineering information engineering symbols Algorithm |
| Zdroj: | 2019 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC). |
| DOI: | 10.1109/icspcc46631.2019.8960766 |
| Popis: | Distributed targets detection in Gaussian noise is studied. Since the noise power is possible to change from cell to cell due to the heterogeneity of the surveillance area, the noise in the test/training data is assumed to have the same structure of covariance matrix (CM) but various powers. Three detectors are derived via the generalized likelihood ratio test, Rao and Wald tests, respectively. The new detectors have the statistical constant false alarm rate (CFAR) properties against the noise power and structure of the noise CM. Monte Carlo simulations show that the new detectors outperform their counterparts. |
| Databáze: | OpenAIRE |
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