Comparison of Two Likelihood-Based Target Detection Methods in the Presence of Interference

In this study, the problem of target detection in the presence of interference using a uniform linear array is considered. The proposed iterative detector is derived based on the generalized likelihood ratio tests (GLRT) principle, assuming that the direction, the complex amplitude and the power of the interfering sources and the received noise variance are all unknown. In the proposed GLR-based detector, first the direction of interference sources are estimated and then by substituting the maximum likelihood (ML) estimates of the other unknown parameters the likelihood ratio is constructed. Another approach for the detection problem is to apply an adaptive beamformer in order to maximally reject the interferences and then a GLR test for the beamformer output has been proposed that is called adaptive beamformer detector. The Comparison between the proposed GLR-based detector and adaptive beamformer detector is done by using computer simulations. The results show that the proposed GLR-based detector performs better than the adaptive beamformer detector, but the GLR-based detector has more computational complexity.