Details

Autor(en) / Beteiligte

• Sun, Guohao
• He, Zishu
• Tong, Jun
• Zhang, Xuejing

Titel

Knowledge-Aided Covariance Matrix Estimation via Kronecker Product Expansions for Airborne STAP

Ist Teil von

• IEEE geoscience and remote sensing letters, 2018-04, Vol.15 (4), p.527-531

Ort / Verlag

Piscataway: IEEE

Erscheinungsjahr

2018

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• This letter proposes a new approach for knowledge-aided estimation of structured clutter covariance matrices (CCMs) in airborne radar systems with limited training data. First, we model the CCM in space-time adaptive processing (STAP) as a sum of low-rank Kronecker products. We then apply a permutation operation to convert the Kronecker factors into linear structures and propose a novel CCM estimation method under the maximum-likelihood framework. Employing a proximal gradient algorithm, the proposed method simultaneously exploits the knowledge about the clutter and the Kronecker structure of the CCM. We finally evaluate the performance of the proposed method using real data from airborne STAP.