A Class of Novel STAP Algorithms Using Sparse Recovery Technique
| dc.creator | Zhang, Hao | |
| dc.creator | Li, Gang | |
| dc.creator | Meng, Huadong | |
| dc.date | 2009-04-08 | |
| dc.date.accessioned | 2026-07-07T13:01:42Z | |
| dc.date.available | 2026-07-07T13:01:42Z | |
| dc.description | A class of novel STAP algorithms based on sparse recovery technique were presented. Intrinsic sparsity of distribution of clutter and target energy on spatial-frequency plane was exploited from the viewpoint of compressed sensing. The original sample data and distribution of target and clutter energy was connected by a ill-posed linear algebraic equation and popular $L_1$ optimization method could be utilized to search for its solution with sparse characteristic. Several new filtering algorithm acting on this solution were designed to clean clutter component on spatial-frequency plane effectively for detecting invisible targets buried in clutter. The method above is called CS-STAP in general. CS-STAP showed their advantage compared with conventional STAP technique, such as SMI, in two ways: Firstly, the resolution of CS-STAP on estimation for distribution of clutter and target energy is ultra-high such that clutter energy might be annihilated almost completely by carefully tuned filter. Output SCR of CS-STAP algorithms is far superior to the requirement of detection; Secondly, a much smaller size of training sample support compared with SMI method is requested for CS-STAP method. Even with only one snapshot (from target range cell) could CS-STAP method be able to reveal the existence of target clearly. CS-STAP method display its great potential to be used in heterogeneous situation. Experimental result on dataset from mountaintop program has provided the evidence for our assertion on CS-STAP. | |
| dc.description | 8 pages, 5 figures | |
| dc.identifier | https://arxiv.org/abs/0904.1313 | |
| dc.identifier | http://arxiv.org/abs/0904.1313 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/226236 | |
| dc.subject | Information Theory | |
| dc.title | A Class of Novel STAP Algorithms Using Sparse Recovery Technique | |
| dc.type | text |