Decentralized CFAR Signal Detection

In cell-averaging constant false-alarm rate detection, CA-CFAR, an estimate of the clutter plus noise from the reference cells surrounding the cell under test is used to set the adaptive threshold. This detection threshold is then scaled by a threshold multiplier in order to achieve the desired probability of false alarm. We develop the theory of CA-CFAR detection using multiple sensors and data fusion, where detection decisions are transmitted from each CA-CFAR detector to the data fusion center. The overall decision is obtained at the data fusion center based on some “k out of n* fusion rule. For a Swerling target model I embedded in a white Gaussian noise of unknown level, we obtain the optimum threshold multipliers of the individual detectors. At the data fusion center, we derive an expression for the overall probability of detection while the overall probability of false alarm is maintained at the desired value for the given fusion rules. An example is presented showing numerical results.