The invention discloses an ISAR (
Inverse Synthetic Aperture Radar)
image rotation speed
estimation method based on sequence continuity, which depends on a SeqFormer
network architecture and is combined with a decomposable instance query mechanism and a cross-frame
feature modeling strategy to fully mine rotation consistency and geometric relevance between adjacent frames. According to the method, the rotation dynamic constraint is established among multiple frames of images, so that unified modeling and accurate analysis of
continuous rotation features of the target are realized. In the
feature extraction stage, a cross-frame feature interaction and
fusion mechanism is utilized, the problem of
local structure discontinuity caused by
noise and shielding is effectively weakened, the morphological integrity of a target in a
time sequence is kept, and stable geometric support is provided for rotation
estimation. Furthermore, modeling and regression learning are carried out on rotation difference features between adjacent frames, the network can directly output the rotation speed of the target, the complex process depending on calibration or key point matching in a traditional method is avoided, and
estimation precision and stability are remarkably improved.