Abstract: Apparatus and method for improving the detection of signals obscured by either correlated Gaussian or non-Gaussian noise plus additive white Gaussian noise using Estimates from multi-channel data of model parameters that describe the noise disturbance correlation are obtained from data that contain signal-free data vectors, referred to as “secondary” or “reference” cell data. These parameters form the coefficients of a multi-channel whitening filter. A data vector to be tested for the presence of a signal passes through the multi-channel whitening filter. The filter's output is then processed to form a test statistic. The test statistic is compared to a threshold value to decide whether a signal is “present” or “absent”. Embodiments of the apparatus and method include estimating the signal amplitude both implicitly and explicitly and calculating test statistics for signal detection in both Gaussian and non-Gaussian noise.

Abstract: A process and system for improving the detection of signals in additive correlated non-Gaussian noise using multichannel data. This improves detection performance of receivers through the signal processing architecture when the observation data X(n) is first split into an upper and lower path. In the upper path, the known signal is first subtracted from the data and then passed directly to the prediction error filter. The output of the prediction error filters are then input to the zero-mean non-linear processor and their associated estimated variances are used to determine H.sub.0 and H.sub.1 where H.sub.0 denotes the condition where no signal is present in the data and H.sub.1 denotes the signal present case.

Abstract: This invention addresses the problem of radar target detection in severely heterogeneous clutter environments. Specifically, we present the performance of the normalized matched filter test in a background of disturbance consisting of clutter having a covariance matrix with known structure and unknown scaling plus background white Gaussian noise. It is shown that when the clutter covariance matrix is low rank, the (LRNMF) test retains invariance with respect to the unknown scaling as well as the background noise level and has an approximately constant false alarm rate (CFAR). Therefore, a technique known as self-censoring reiterative fast maximum likelihood/adaptive power residue (SCRFML/APR) is developed to treat this problem and its performance is discussed. The SCRFML/AP method is used to estimate the unknown covariance matrix in the presence of outliers. This covariance matrix estimate can then be used in the LRNAMF or any other eigen-based adaptive processing technique.