.png){kind=icon}
.png){kind=icon}
Return
A NEW PARAMETER ESTIMATION METHOD FOR GTD MODEL BASED ON MODIFIED COMPRESSED SENSING
.png){kind=icon}
OA
AI
DOI：10.2528/PIER13052017.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The electromagnetic scattering mechanism of radar targets in the high-frequency domain can be characterized exactly by geometrical theory of diffraction (GTD) model. In this paper, we propose a novel parameter estimation method for GTD model based on compressed sensing. The sparse characteristic of radar echoes is analyzed, and the parameter estimation problem is converted to one of sparse signal reconstruction. Furthermore, clustering and linear least-minimum-squares algorithms are utilized to improve the accuracy of the result. Compared with several modern spectral estimation techniques, the proposed method gives a more precise estimation of the GTD model parameters, especially the scattering centers. Simulations with synthetic and measured data in an anechoic chamber confirm the effectiveness of the method.
Keywords:
RADAR
RECONSTRUCTION
EXTRAPOLATION
RECOVERY
ESPRIT
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
P
IF:
9.3
Papers:
2.9K
Citations:
2.7K