Adaptive Radar Detection without Secondary Data: Exploiting a Diversity Idea

This paper addresses adaptive detection of possibly range-spread targets without resorting to secondary data, namely data free of signal components, but sharing the spectral properties of those under test. More precisely, detection of coherent target echoes embedded in Gaussian noise with unknown covariance matrix has been attacked by approximating the covariance matrix of the noise as a block diagonal one with identical diagonal blocks; moreover, each block is estimated from a set of subvectors obtained by filtering out possible signal components from data under test. The proposed detector guarantees the Constant False Alarm Rate (CFAR) property with respect to the noise power. In addition, its threshold setting seems not very sensitive to the actual structure of covariance matrix. Finally, a preliminary performance assessment, also conducted using real high range resolution radar data from an urban scene, has shown that it might be a viable means to deal with uncertain scenarios.