Abstract:

The knowledge-aided secondary data selection method is a model-based algorithm, but its performance degrades significantly when there is a mismatch due to array errors between the assumed clutter model and the received data. To address this issue, an approach based on the subspace for array calibration is presented. Firstly, the orthogonal complement subspace of clutter can be represented using the radar geometry parameter. Then, a left singular vector that corresponds to the maximal singular value is constructed by decomposition of the received data. Finally, using the orthogonality between them, the array error can be estimated. Simulation results show that this method can estimate array errors accurately, and improve the robustness of the knowledge-aided secondary data selection method.