Paper
6 May 2024 An adaptive censoring CFAR detector in non-homogeneous environments
Lulin Wang, Xingyu Mao, Guiru Liu, Jian Sun
Author Affiliations +
Proceedings Volume 13107, Fourth International Conference on Sensors and Information Technology (ICSI 2024); 1310702 (2024) https://doi.org/10.1117/12.3029371
Event: Fourth International Conference on Sensors and Information Technology (ICSI 2024), 2024, Xiamen, China
Abstract
Because variability index CFAR (VI-CFAR) and switching variability index CFAR (SVI-CFAR) detectors are affected by the number and location of interference targets and the number of clutter reference cells, a nonhomogeneous environment cannot be recognized accurately, resulting in a detection performance decline in a nonhomogeneous environment. This paper proposes an adaptive censoring CFAR detector (AC-CFAR), which first calculates the position of the transition and then identifies whether the subreference window starting from the transition is in a homogeneous environment. Then, based on the position of the transition and whether the subreference window is in a homogeneous environment, an appropriate method was selected from CA-CFAR, GO-CFAR and OS-CFAR to calculate the detection threshold. Monte Carlo simulation results show that the detection performance of AC-CFAR is consistent with that of VI-CFAR and SVI-CFAR, and near that of CA-CFAR in a homogeneous environment, but its performance is better than that of VI-CFAR and SVI-CFAR in a multitarget environment with a large number of interference targets. In particular, the number of interference targets is uneven on both sides of the cell being tested. In a clutter edge environment with less clutter, the false alarm rate of AC-CFAR is marginally lower than that of VI-CFAR and SVI-CFAR.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Lulin Wang, Xingyu Mao, Guiru Liu, and Jian Sun "An adaptive censoring CFAR detector in non-homogeneous environments", Proc. SPIE 13107, Fourth International Conference on Sensors and Information Technology (ICSI 2024), 1310702 (6 May 2024); https://doi.org/10.1117/12.3029371
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Target detection

Monte Carlo methods

Background noise

Switching

Back to Top