Abstract
Sparse-signal processing (SSP) is interpreted in this paper as a sparse model-based refinement of typical steps in radar processing. Matched filtering remains vital within SSP but joined with radar detection promoting the sparsity. Realistic measurements are also supported in SSP by using MonteCarlo (MC) methods. MC-based SSP promotes the sparsity by detection-driven MC-sampling that also improves efficiency. This MC extension aims for the stochastic description of sparse solutions, and the flexibility to use any prior on signals or on data acquisition, as well as any distribution of noise or clutter. Numerical experiments demonstrate favorable performance of the proposed SSP.
| Original language | English |
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| Title of host publication | 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 5008-5011 |
| Number of pages | 4 |
| ISBN (Print) | 9781479928927 |
| DOIs | |
| Publication status | Published - 2014 |
| Event | 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence, Italy Duration: 4 May 2014 → 9 May 2014 |
Other
| Other | 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 |
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| Country/Territory | Italy |
| City | Florence |
| Period | 4/05/14 → 9/05/14 |
Keywords
- compressive sensing
- detection
- non-Gaussian distribution
- radar systems
- sparse recovery