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

翻译：在自适应信号检测中，通常难以获取足够的训练数据来估计未知的噪声协方差矩阵。此外，干扰的普遍存在进一步增加了检测问题的复杂性。本文提出一种两步法——即检测前干扰消除（ICBD），以解决未知高斯噪声与子空间干扰环境中的信号检测问题。第一步将测试数据与训练数据投影至干扰正交子空间，从而抑制干扰；第二步则采用传统自适应检测器的设计思想。由于投影后数据维度的降低，基于ICBD的检测器可仅使用少量训练数据实现有效工作。相较于传统检测器，ICBD具有两大额外优势：一是计算负担更轻，二是能妥善处理训练数据中包含干扰的情形。本文还给出了ICBD检测器的统计特性，并证明了在训练数据充足且无干扰的特殊情况下，其与传统检测器等价。