Passive Multi-Channel Detection: A General First-Order Statistical Theory

In this paper a general first-order statistical framework is established for passive multi-channel detection and localization of an unknown radiated signal. This signal written in terms finite basis expansion, the map between these coefficients measured data on sensor channel that might be known, partially or except dimension subspace. The noise at each assumed to Gaussian white; variances may equal, possibly unequal. develops detectors all nine cases. These are generalized likelihood ratios, typically decompose into locally computed detector statistics plus pairwise coherence, cross-validation, statistics. Of particular note scale-invariant preserve constant false alarm rate (CFAR) property with respect power.