Parametric Wald test for target detection with distributed MIMO radar in partially mixing homogeneous and non‐homogeneous environments

Abstract

Abstract This study focusses on the target detection problem in the distributed multiple‐input multiple‐output (MIMO) radar. The observation environment is assumed to be partially homogeneous in one transmitter‐receiver path and simultaneously non‐homogeneous for different paths. By characterising the disturbance signals with a series of auto‐regressive (AR) processes, two parametric detectors based on the Wald test are developed. The first one utilises an iterative scheme to estimate the unknown parameters that were involved in the derivation, and the second one obtains these estimations sequentially. One of the most attractive features of the two distributed MIMO radar detectors is that they can be used in the presence/absence of the training data. More specifically, they can take advantage of the training data to boost the detection performances effectively even if the training data are very limited. The simulation results of several scenarios demonstrate the remarkable detection performances of authors’ proposed methods compared with that of the competitors. Furthermore, the amplitude estimation performances of the two detectors are evaluated via the corresponding Cramér–Rao bound (CRB).