Detector Design and Performance Analysis for Target Detection in Subspace Interference

TL;DR

This paper introduces the ICBD method for target detection in Gaussian noise with subspace interference, effectively suppressing interference and enabling detection with limited training data, while reducing computational complexity.

Contribution

The paper proposes a novel interference cancellation before detection (ICBD) approach that improves detection performance and efficiency in interference-laden environments.

Findings

ICBD effectively suppresses interference in the detection process.

ICBD-based detectors require less training data and computational resources.

They are statistically equivalent to traditional detectors with ample interference-free training data.

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