Abstract
A novel HRRP probability density estimate method, namely Gamma-SLC, is presented by combining Gamma model and stochastic learning of the cumulative (SLC). The presented method has the advantages of high pertinence and high accuracy from Gamma distribution and high adaptability from SLC, but avoids the disadvantage of both. In addition, a new criterion for evaluating the estimation of probability density is designed based on maximum-entropy non-Gaussian measurement. Experimental results using outfield real data demonstrate the validity of the presented method.
| Original language | English |
|---|---|
| Pages (from-to) | 438-443 |
| Number of pages | 6 |
| Journal | Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics |
| Volume | 30 |
| Issue number | 3 |
| State | Published - Mar 2008 |
| Externally published | Yes |
Keywords
- High resolution range profile (HRRP)
- Maximum-entropy principle
- Probability density estimation
- Stochastic learning of the cumulative (SLC)