TY - JOUR

T1 - Parametric space-time detection and range estimation of a small target

AU - Hao, Chengpeng

AU - Gazor, Saeed

AU - Orlando, Danilo

AU - Foglia, Goffredo

AU - Yang, Jun

N1 - Publisher Copyright: © The Institution of Engineering and Technology 2015.

PY - 2015/2/1

Y1 - 2015/2/1

N2 - In this study, the authors deal with the problem of parametric detection for relatively small targets using space-time adaptive processing (STAP). In contrast to the existing parametric STAP detectors, the proposed detectors perform range estimation by exploiting the spillover of the target energy between consecutive samples. To this end, the authors assume that the received useful signal is known up to a complex unknown deterministic factor parameter and the disturbance signal is modelled as a multichannel autoregressive Gaussian process. Moreover, the authors assume that a set of secondary data is available which are free of signal components, but have the same unknown parameters as the disturbance in the cells under test. Using these assumptions, the so-called simplified generalised likelihood ratio test (GLRT) and the two-step GLRT are derived and assessed. It is worth noting that the simplified GLRT is based on an asymptotic ML estimate of the amplitude, which leads to a simple and closed-form detection statistic. The performance assessment, conducted resorting to both simulated dataset and KASSPER dataset, has shown that the proposed decision schemes can provide accurate estimates of the target position within the cell under test and ensure enhanced detection performance compared with their natural competitors.

AB - In this study, the authors deal with the problem of parametric detection for relatively small targets using space-time adaptive processing (STAP). In contrast to the existing parametric STAP detectors, the proposed detectors perform range estimation by exploiting the spillover of the target energy between consecutive samples. To this end, the authors assume that the received useful signal is known up to a complex unknown deterministic factor parameter and the disturbance signal is modelled as a multichannel autoregressive Gaussian process. Moreover, the authors assume that a set of secondary data is available which are free of signal components, but have the same unknown parameters as the disturbance in the cells under test. Using these assumptions, the so-called simplified generalised likelihood ratio test (GLRT) and the two-step GLRT are derived and assessed. It is worth noting that the simplified GLRT is based on an asymptotic ML estimate of the amplitude, which leads to a simple and closed-form detection statistic. The performance assessment, conducted resorting to both simulated dataset and KASSPER dataset, has shown that the proposed decision schemes can provide accurate estimates of the target position within the cell under test and ensure enhanced detection performance compared with their natural competitors.

UR - http://www.scopus.com/inward/record.url?scp=84923261683&partnerID=8YFLogxK

U2 - 10.1049/iet-rsn.2014.0081

DO - 10.1049/iet-rsn.2014.0081

M3 - Journal article

AN - SCOPUS:84923261683

VL - 9

SP - 221

EP - 231

JO - IET Radar, Sonar and Navigation

JF - IET Radar, Sonar and Navigation

SN - 1751-8784

IS - 2

ER -