Weighted eigenvector method for sparse-aperture ISAR phase error correction

The traditional eigenvector method for autofocus can not obtain ideal results in low-SNR (signal-to-noise ratio) cases, because the contribution of the signal in different range bins to the final signal differs greatly. Thus, a weighted eigenvector method for ISAR(inverse synthetic aperture radar) phase error correction is proposed by adding different weights to each range bin according to its SNR. The method can not only deal with normal ISAR signals, but also can handle evenly under-sampled or block sparse, even unevenly sparse data. Finally, actual data processing results confirm the validity of the proposed algorithm.