Detector Design and Performance Analysis for Target Detection in Subspace Interference
Abstract
It is often difficult to obtain sufficient training data for adaptive signal detection, which is required to calculate the unknown noise covariance matrix. Additionally, interference is frequently present, which complicates the detecting issue. We provide a two-step method, termed interference cancellation before detection (ICBD), to address the issue of signal detection in the unknown Gaussian noise and subspace interference. The first involves projecting the test and training data to the interference-orthogonal subspace in order to suppress the interference. Utilizing traditional adaptive detector design ideas is the next stage. Due to the smaller dimension of the projected data, the ICBD-based detectors can function with little training data. The ICBD has two additional benefits over traditional detectors. Lower computational burden and proper operation with interference being in the training data are two additional benefits of ICBD-based detectors over conventional ones. We also give the statistical properties of the ICBD-based detectors and demonstrate their equivalence with the traditional ones in the special case of a large amount of training data containing no interference
Keywords
Cite
@article{arxiv.2304.07005,
title = {Detector Design and Performance Analysis for Target Detection in Subspace Interference},
author = {Weijian Liu and Jun Liu and Tao Liu and Hui Chen and Yong-Liang Wang},
journal= {arXiv preprint arXiv:2304.07005},
year = {2023}
}
Comments
This manuscript is submitted to IEEE SPL with paper ID SPL-35580-2023 and the decision "AQ - Publish In Minor, Required Changes"