Application of cone detectors to detection of signals in structured interference

The paper addresses a specific case of the detection of signals in noisy environments. In the situation to be considered, noise is modeled as additive Gaussian noise. Signals, and interference of known structure, are modeled as belonging to linear subspaces. The paper reviews and extends previous work on this problem by applying cone classes to model additional information of the relative energy of signals to structured interferences. Detectors designed with this extended model are compared to classical subspace detectors. An example is given to illustrate possible improvements using this new detection scheme. Such an example is also applicable to the multisensor array problem in which windowing and sequencing signal areas of the array record can produce periodic sequences.