Interference Target Detection Research Articles

Modified Rao test for distributed target detection in interference and noise

This paper studies the problem of adaptive detecting distributed targets embedded in structured interference and colored noise. The signal and interference are assumed to lie in two linearly independent subspaces with unknown coordinates. The relative parameter of the conventional Rao test contains only the signal coordinate, whereas it is modified to comprise both the signal and interference coordinates in this paper. This modified Rao test has the constant false alarm rate (CFAR) property and the capability of interference rejection. Several numerical results show that this modified Rao test exhibits more reliable detection performance than the conventional Rao test; it also outperforms the generalized likelihood ratio test (GLRT) in some scenarios.

Bayesian generalised likelihood ratio tests for distributed target detection in interference and noise

This study addresses the problem of detecting a distributed target in interference and noise with low sample support. The target signal and interference lie in two linearly independent subspaces and the spectral property of the noise is unknown. The number of available training data is too small to form a non-singular sample covariance matrix. To overcome the difficulty, the authors resort to the Bayesian framework and design two generalised likelihood ratio tests. Both detectors can reduce training requirement by utilising the a prior knowledge of the noise covariance matrix. Numerical results show that the proposed detectors provide better detection performance than the conventional solutions in training-limited scenarios.

Rao tests for distributed target detection in interference and noise

This paper deals with the problem of detecting a distributed target in interference and noise. The target signal and interference are assumed to lie in two linearly independent subspaces, and their coordinates are unknown. The noise is Gaussian distributed, with an unknown covariance matrix. To estimate the covariance matrix, a set of training data is supposed available. We derive the Rao test and its two-step variant both in homogeneous and partially homogeneous environments. All of the proposed detectors exhibit a desirable constant false alarm rate. Numerical examples show that the proposed detectors can provide better detection performance than their natural counterparts in some scenarios.