An Efficient Attributed Scattering Center Extraction Method Accelerated by Dictionary Reuse

The attributed scattering center (ASC) model provides a concise and physically relevant description of the targets and plays a vital role in inverse scattering problems. However, ASC extraction is a high-dimensional problem, which involves complex computations and is memory-demanding. In this letter, an efficient attributed scattering center extraction method is proposed. The ASCs are extracted one by one, with alternative optimization integrated into it to reduce dictionary dimension. Two properties of the ASC model, namely superposition property and norm-invariant property, are revealed. The superposition property and norm-invariant property are utilized to reuse dictionaries and to reduce the computational amount of l₂-norm, respectively, which accelerates the ASC extraction procedure. The numerical example shows that the average time to extract one ASC is less than 0.2 s, which verifies the effectiveness of the proposed method.