Adaptive Double Subspace Signal Detection in Gaussian Background-Part I: Homogeneous Environments

作者：Liu, Weijian^*; Xie, Wenchong; Liu, Jun; Wang, Yongliang

来源：IEEE Transactions on Signal Processing, 2014, 62(9): 2345-2357.

DOI：10.1109/TSP.2014.2309556

• Constant false alarm rate (CFAR)
• double subspace signal
• generalized cosine-squared
• homogeneous environments
• multidimensional signal
• signal mismatch

In this two-part paper, we consider the problem of adaptive multidimensional/multichannel signal detection in Gaussian noise with unknown covariance matrix. The test data (primary data) is assumed as a collection of sample vectors, arranged as the columns of a rectangular data array. The rows and columns of the signal matrix are both assumed to lie in known subspaces, but with unknown coordinates. Due to this feature of the signal structure, we name this kind of signal as the double subspace signal. Part I of this paper focuses on the adaptive detection in homogeneous environments, while Part II deals with the adaptive detection in partially homogeneous environments. Precisely, in this part, we derive the generalized likelihood ratio test (GLRT), Rao test, Wald test, as well as their two-step variations, in homogeneous environments. Three types of spectral norm tests (SNTs) are also introduced. All these detectors are shown to possess the constant false alarm rate (CFAR) property. Moreover, we discuss the differences between them and show how they work. Another contribution is that we investigate various special cases of these detectors. Remarkably, some of them are well-known existing detectors, while some others are still new. At the stage of performance evaluation, conducted by Monte Carlo simulations, both matched and mismatched signals are dealt with. For each case, more than one scenario is considered.

• 出版日期2014-5
• 单位西安电子科技大学; 中国人民解放军国防科学技术大学; 中国人民解放军空军预警学院