Signal Learning In The Affine Domain By Compressed Sensing
- Resource Type
- Conference
- Authors
- Lu, Yun; Statz, Christoph; Plettemeier, Dirk
- Source
- 2019 20th International Radar Symposium (IRS) Radar Symposium (IRS), 2019 20th International. :1-7 Jun, 2019
- Subject
- Aerospace
Components, Circuits, Devices and Systems
Fields, Waves and Electromagnetics
Geoscience
Photonics and Electrooptics
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Radar
Interference
Time-frequency analysis
Noise measurement
Transforms
Compressed sensing
Signal detection
- Language
- ISSN
- 2155-5753
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.