Pallotta, Luca (2014) Covariance Matrix Estimation for Radar Applications. [Tesi di dottorato]
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| Item Type: | Tesi di dottorato |
|---|---|
| Resource language: | English |
| Title: | Covariance Matrix Estimation for Radar Applications |
| Creators: | Creators Email Pallotta, Luca luca.pallotta@unina.it |
| Date: | 28 March 2014 |
| Number of Pages: | 140 |
| Institution: | Università degli Studi di Napoli Federico II |
| Department: | Ingegneria Elettrica e delle Tecnologie dell'Informazione |
| Scuola di dottorato: | Ingegneria dell'informazione |
| Dottorato: | Ingegneria elettronica e delle telecomunicazioni |
| Ciclo di dottorato: | 26 |
| Coordinatore del Corso di dottorato: | nome email Rinaldi, Niccolò nirinald@unina.it |
| Tutor: | nome email De Maio, Antonio UNSPECIFIED |
| Date: | 28 March 2014 |
| Number of Pages: | 140 |
| Keywords: | covariance matrix estimation, adaptive radar receiver |
| Settori scientifico-disciplinari del MIUR: | Area 09 - Ingegneria industriale e dell'informazione > ING-INF/03 - Telecomunicazioni |
| Date Deposited: | 08 Apr 2014 11:29 |
| Last Modified: | 27 Jan 2015 11:42 |
| URI: | http://www.fedoa.unina.it/id/eprint/9710 |
Collection description
In a typical radar system, the power of the useful signal component is tipically lower than the competing disturbance strength; consequently, radar detection becomes a very challenging problem. The SINR (Signal to Interference plus Noise Ratio) is generally the most critical figure of merit when designing a radar filter. The processor that maximizes the output SINR is a coherent, linear, transversal filter, based on the exact knowledge of the true disturbance covariance matrix. However, in real radar systems, this requirement cannot be satisfied and an estimate of the covariance matrix is adopted instead of the exact one, leading to the so-called adaptive radars. The aim of this thesis is the introduction of innovative covariance matrix estimation techniques, operating in different conditions. In particular, a covariance matrix estimator, based on statistical argumentations, is presented when homogeneous secondary data are available. Moreover, exploiting geometric considerations, two family of covariance estimators are defined and adopted for training data selection, which is useful when some outliers affect the secondary data. Finally, a family of radar receivers for extended targets in range, enforcing several structures over the disturbance covariance, is described, which is effective when it is not possible to identify secondary data free of targets.
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