Compound Gaussian Radar Clutter Model With Positive Tempered Alpha-Stable Texture
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916511098077184 |
|---|---|
| author | Liao, Xingxing Xie, Junhao Zhou, Jie |
| author_facet | Liao, Xingxing Xie, Junhao Zhou, Jie |
| contents | The compound Gaussian (CG) family of distributions has achieved great success in modeling sea clutter. This work develops a flexible-tailed CG model to improve generality in clutter modeling, by introducing the positive tempered $α$-stable (PT$α$S) distribution to model clutter texture. The PT$α$S distribution exhibits widely tunable tails by tempering the heavy tails of the positive $α$-stable (P$α$S) distribution, thus providing greater flexibility in texture modeling. Specifically, we first develop a bivariate isotropic CG-PT$α$S complex clutter model that is defined by an explicit characteristic function, based on which the corresponding amplitude model is derived. Then, we prove that the amplitude model can be expressed as a scale mixture of Rayleighs, just as the successful compound K and Pareto models. Furthermore, a characteristic function-based method is developed to estimate the parameters of the amplitude model. Finally, real-world sea clutter data analysis indicates the amplitude model's flexibility in modeling clutter data with various tail behaviors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_05174 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Compound Gaussian Radar Clutter Model With Positive Tempered Alpha-Stable Texture Liao, Xingxing Xie, Junhao Zhou, Jie Signal Processing Methodology The compound Gaussian (CG) family of distributions has achieved great success in modeling sea clutter. This work develops a flexible-tailed CG model to improve generality in clutter modeling, by introducing the positive tempered $α$-stable (PT$α$S) distribution to model clutter texture. The PT$α$S distribution exhibits widely tunable tails by tempering the heavy tails of the positive $α$-stable (P$α$S) distribution, thus providing greater flexibility in texture modeling. Specifically, we first develop a bivariate isotropic CG-PT$α$S complex clutter model that is defined by an explicit characteristic function, based on which the corresponding amplitude model is derived. Then, we prove that the amplitude model can be expressed as a scale mixture of Rayleighs, just as the successful compound K and Pareto models. Furthermore, a characteristic function-based method is developed to estimate the parameters of the amplitude model. Finally, real-world sea clutter data analysis indicates the amplitude model's flexibility in modeling clutter data with various tail behaviors. |
| title | Compound Gaussian Radar Clutter Model With Positive Tempered Alpha-Stable Texture |
| topic | Signal Processing Methodology |
| url | https://arxiv.org/abs/2412.05174 |