A Review of Sparsity-Based Methods for Analysing Radar Returns from Helicopter Rotor Blades
Abstract
Radar imaging of rotating blade-like objects, such as helicopter rotors, using narrowband radar has lately been of significant interest; these objects cannot be adequately described by the classic point-scatterer model. Recently, a novel `tilted-wire' scatterer model has been developed that can provide an accurate and sparse representation of radar returns from such objects. Following a literature review on compressed sensing algorithms, covering both greedy and lp minimisation methods (0 < p<- 1), the report focuses on a comparative study of various greedy pursuit algorithms, using both simulated and real radar data, with a particular emphasis on the use of the tilted-wire scatterer model. It is observed that the greedy algorithms that select multiple atoms at the matched-filtering stage do not perform well when the atoms used in the dictionary are significantly correlated. Amongst the greedy algorithms, Orthogonal Matching Pursuit (OMP) exhibits the best performance, closely followed by Conjugate Gradient Pursuit (CGP), which has a much smaller computational complexity than OMP. In applications where the tilted-wire model requires large dictionaries and large CPI atoms, CGP is the preferred option.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 01, 2016
- Accession Number
- AD1024204
Entities
People
- Hai-tan Tran
- Kutluyil Dogancay
- Ngoc H. Nguyen
- Rocco Melino
Organizations
- Defence Science and Technology Group