Three types of distributed CFAR detection based on local test statistic

Abstract

Three types of distributed constant false-alarm rate (CFAR) detection based on local test statistic (LTS) are developed under the condition that all the samples in different sensors are independent and identically distributed (IID). They are called R (ratio), S (substract) and P (plus) types. For a two-sensor network and local order statistics CFAR (OS-CFAR) algorithm, we derive their closed-form expressions for false alarm and detection probabilities of detecting a Swerling II target in Gaussian noise. The numerical results show that ρ, the ratio between the noise power levels in two local sensors, has very strong effects on the performances of the S and P type schemes and the centralized detection. They can't hold to be CFAR when there isn't the precise prior knowledge of ρ. Compared with the centralized detection, the distributed detection scheme is more robust for the effect of ρ. Fortunately, the R type scheme is not affected by the ρ at all. It implies that the local CFAR processing can immunize the distributed detection against the ρ.