Adaptive signal detection in partially-known interference and unknown Gaussian noise

Besides the noise and possible target signal, the data received by radar often contains interference, which can significantly degrade the detection performance. This paper considers the problem of detecting a target in partially-known interference and unknown Gaussian noise. The interference is unknown, but it is constrained in a subspace orthogonal to the signal in the whitened space. According to the generalized likelihood ratio test (GLRT) criterion, we proposed an effective detector, which has improved detection performance than the corresponding Rao and Wald tests. Remarkably, the GLRT in the presence of interference has the same form as that in the absence of interference. However, the interference affects the detection performance of the detector. The effectiveness of the proposed detector is demonstrated by Monte Carlo simulations.