Signal Learning In The Affine Domain By Compressed Sensing
| Autor: | Christoph Statz, Yun Lu, Dirk Plettemeier |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
020301 aerospace & aeronautics
Property (programming) Computer science 020206 networking & telecommunications 02 engineering and technology Signal symbols.namesake Fourier transform Compressed sensing Wavelet 0203 mechanical engineering Simple (abstract algebra) 0202 electrical engineering electronic engineering information engineering symbols Detection theory Affine transformation Algorithm |
| Zdroj: | 2019 20th International Radar Symposium (IRS). |
| DOI: | 10.23919/irs.2019.8768090 |
| Popis: | Compressed sensing (CS) [1] has achieved large success in the past years for signal detection and estimation (e.g. in microwave radar techniques). One of the preconditions for a successful application of CS is that the corresponding signal must exhibit the ”simple” (sparse or low-rank) property in particular domains (e.g. Fourier, Wavelet etc.). Unfortunately, this simple property is not always directly available. In this paper, we introduce a framework which bases on double low-rank analysis to pursuit the simple signal property. Practical results from a moving-receiver moving-transmitter configuration show that double low-rank signal learning provides very promising performance in radar signal detection and estimation. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|