@inproceedings{f1e1275b5ccc493cb6709ada0f73c537,
title = "Improved AR-Model-Based Rao Test in Complex Gaussian Clutter",
abstract = "Our study considers the adaptive detection of point targets in compound Gaussian clutter which is in possession of unknown covariance matrix. To overcome the performance degradation problem which is aroused by the limited number of training data, the autoregressive (AR) process is used for the modeling of the speckle component. We first derive the Rao detector under the assumption of known covariance matrix of the clutter, and then reconstruct it by AR parameters resorting to the matrix factorization. Meanwhile, the newly derived detector is proved asymptotically constant false alarm rate with respect to the clutter covariance matrix. Finally, simulation results have confirmed the effectiveness in small data record case of the newly derived detector.",
keywords = "Rao test, autoregressive, compound Gaussian",
author = "Jiabao Liu and Meiguo Gao and Jihong Zheng and Haoxuan Xu",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 4th IEEE International Conference on Electronics Technology, ICET 2021 ; Conference date: 07-05-2021 Through 10-05-2021",
year = "2021",
month = may,
day = "7",
doi = "10.1109/ICET51757.2021.9451052",
language = "English",
series = "2021 IEEE 4th International Conference on Electronics Technology, ICET 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "800--805",
booktitle = "2021 IEEE 4th International Conference on Electronics Technology, ICET 2021",
address = "United States",
}