Polarity Coincidence Array detectors (PCA) are considered for testing the hypothesis that a random signal is common to an array of receivers which contain noise processes that are independent representations of a given class of stochastic processes. A standard procedure is to reduce the received data by sampling and then hard limiting. Hard limiting is shown to introduce an inherent loss in input signal power of 1.96 dB when the input data is a sequence of independent samples from a stationary Gaussian process. However, when the stationary and/or Gaussian assumptions are violated, the relative efficiencies of the PCA detectors can greatly improve. When the input samples are dependent, it is necessary to assume Ganssian inputs in order to analyze the PCA detectors. However, these devices are still unaffected by a nonstationary noise level that is slowly varying relative to the inverse bandwidth of the pre-filter. Furthermore, the loss due to clipping is considerably reduced as the sample dependence (i.e., sampling rate) increases. For rapid sampling rates, the spectral shapes of the inputs must be known accurately in order to fix the false-alarm rate at some pre-assigned value.
['Morton Kanefsky']
Detection of weak signals with polarity coincidence arrays