Adaptive Detection in Interference
Jun Liu, Danilo Orlando, Chengpeng Hao, Weijian Liu · 2022
This chapter discusses the problem of detecting subspace signals in the presence of subspace interference and Gaussian noise by using multiple observations collected from multiple range cells, bands, and/or coherent processing intervals. Persymmetry is exploited to design one-step and two-step detectors, according to the criterion of generalized likelihood ratio test. Both the detectors exhibit constant false alarm rate properties against the noise covariance matrix. Moreover, the statistical characterizations of the one-step detector are obtained. Exact expressions are derived for the probability of false alarm of the one-step detector in six cases where the signal subspace dimension is no more than 4 or the number of observations is no more than 2. In other cases, an approximate expression is obtained for the probability of false alarm of the one-step detector. Numerical examples illustrate that the designed detectors outperform their counterparts, especially when the number of training data is small. In addition, the one-step generalized likelihood ratio test detector generally has better detection performance than the two-step generalized likelihood ratio test detector.