Bistatic Radar Coincidence Imaging Based on Multiple Measurement Vectors for Rotating Cone-Shaped Targets

Abstract EN

Bistatic radar imaging can overcome limitations of monostatic radar imaging and obtain abundant target feature information; thus, it is followed with interest.

Different from bistatic inverse synthetic aperture radar (Bi-ISAR) imaging, bistatic radar coincidence imaging (Bi-RCI) provides a new tack on the bistatic radar imaging technique.

In this paper, a Bi-RCI based on multiple measurement vectors (MMV) for rotating cone-shaped targets is proposed to realize Bi-RCI coherent processing and improve imaging performance.

Based on the mixed mode signals, a MMV parametric model is established and measurement number coarse selection is proposed.

Finally, a modified sparse Bayesian learning (MSBL) algorithm is introduced to reconstruct the target image.

Simulation results demonstrate the validity and the superiority of the proposed method.