Detection of number of sources via exploitation of centro-symmetry property

Forward/backward (FB) averaging is employed when possible in sensor array processing to improve parameter estimation accuracy by exploiting a centro-symmetry of the signal subspace. While FB averaging effectively doubles the number of data samples, it also introduces intersample correlation. This correlation complicates not only the performance analysis, but the detection of the number of sources as well. It is shown how existing detection procedures such as sequential hypothesis (or likelihood ratio) tests, minimum description length criterion (MDL), and AIC (Akaike information criterion) can be modified when FB averaging is used