کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4973845 | 1451711 | 2017 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Matrix CFAR detectors based on symmetrized Kullback-Leibler and total Kullback-Leibler divergences
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
پردازش سیگنال
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Matrix CFAR detectors based on symmetrized Kullback-Leibler and total Kullback-Leibler divergences Matrix CFAR detectors based on symmetrized Kullback-Leibler and total Kullback-Leibler divergences](/preview/png/4973845.png)
چکیده انگلیسی
Target detection in clutter is a fundamental problem in radar signal processing. When the received radar signal contains only few pulses, it is difficult to achieve a satisfactory performance using the traditional detection algorithm. In recent times, a generalized constant false alarm rate (CFAR) detector on the Riemannian manifold of Hermitian positive-definite (HPD) matrix was proposed. The employment of this detector, which compares the Riemannian distance between the covariance matrix of the cell under test (CUT) and an average matrix of reference cells with a given threshold, has significantly improved the detection performance. However, the application of this detector in real scenarios is still limited by two problems; it is computationally expensive and the detection performance is not very good since the Riemannian distance is utilized. In this paper, the symmetrized Kullback-Leibler (sKL) and the total Kullback-Leibler (tKL) divergences, instead of the Riemannian distance, are used as dissimilarity measures in the matrix CFAR detector. According to sKL and tKL divergences, three average matrices, the sKL mean, the sKL median, and the tKL t center, are derived. Furthermore, the relationship between the detection performance and the anisotropy of the distance measure used in the matrix CFAR detector is explored. Numerical experiments and real radar sea clutter data are given to confirm the superiority of the proposed algorithms in terms of the computational complexity and the detection performance.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Digital Signal Processing - Volume 69, October 2017, Pages 106-116
Journal: Digital Signal Processing - Volume 69, October 2017, Pages 106-116
نویسندگان
Xiaoqiang Hua, Yongqiang Cheng, Hongqiang Wang, Yuliang Qin, Yubo Li, Wenpeng Zhang,