Name
Title | ||
|---|---|---|
A Persymmetric GLRT for Adaptive Detection in Compound-Gaussian Clutter With Random Texture |
Abstract | ||
|---|---|---|
We focus on the problem of detecting a signal in compound-Gaussian clutter, where the texture is a random variable with Gamma or inverse Gamma distribution. The persymmetric structure of the covariance matrix is exploited and a persymmetric generalized likelihood ratio test (Per-GLRT) using a three-step procedure is proposed. In addition, we prove that the Per-GLRT ensures constant false alarm rate (CFAR) property with respect to the covariance matrix. Finally, the detector is assessed by Monte Carlo simulations. Performance comparison of the Per-GLRT with the traditional GLRT shows that the former improves the detection performance in training-limited scenarios. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/LSP.2013.2259232 | IEEE Signal Process. Lett. |
Keywords | Field | DocType |
adaptive detection,random processes,compound-gaussian clutter,inverse gamma distribution,monte carlo simulation,maximum likelihood estimation,compound-gaussian,covariance matrices,gamma distribution,persymmetric generalized likelihood ratio test,per-glrt,persymmetric structure,monte carlo methods,gaussian processes,glrt,constant false alarm rate,persymmetric glrt,cfar,signal detection,covariance matrix,random texture,estimation,clutter,shape,radar,detectors,vectors | Monte Carlo method,Random variable,Likelihood-ratio test,Pattern recognition,Clutter,Gaussian,Artificial intelligence,Constant false alarm rate,Covariance matrix,Inverse-gamma distribution,Mathematics | Journal |
Volume | Issue | ISSN |
20 | 6 | 1070-9908 |
Citations | PageRank | References |
7 | 0.46 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yongchan Gao | 1 | 56 | 7.41 |
| Guisheng Liao | 2 | 996 | 126.36 |
| Shengqi Zhu | 3 | 353 | 26.46 |
| Dong Yang | 4 | 116 | 18.09 |