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Micromotion features of a target contain unique structural information and motion information about the target, which can be used as an important basis for target recognition. One of the technical bottlenecks for micromotion feature extraction is achieving a fine characterization of the target motion state. With the high-precision range and velocity measurement, this paper uses the phase-derived method to reconstruct the motion state of the target and realize accurate translational compensation for the target. At the same time, the high range resolution of wideband radar is used to effectively extract target scattering points with micromotion from the perspective of the one-dimensional high range resolution profile and the two-dimensional inverse synthetic aperture radar (ISAR) image. On this basis, reliable micromotion feature extraction can be achieved by using the slow variability of the target motion and eliminating the translational or rotational information of the target. The performance of the high-precision range and velocity measurement and the feasibility of the micromotion feature extraction based on phase-derived range and velocity measurement are verified by simulation results. Then, the effectiveness of the proposed algorithm is further verified by carrying out a steel ball ejection experiment and a civil aviation aircraft experiment.

The existing methods for motion compensation in inverse synthetic aperture radar (ISAR) imaging are generally limited to the low-order target motion model, and require iterative optimization with limited velocity estimate precision and heavy computational burdens. This paper proposes a high-precision method of phase-derived velocity measurement (PDVM) and applies it to motion compensation of ISAR imaging. The method applies PDVM based on range profiles cross correlation to the translational velocity estimation of targets, and converts the velocity measurement results to the corresponding range increment. The equivalent phase-derived range measurement precision can reach the order of magnitude of millimeter (mm) or even sub-mm, which can satisfy the precision requirements of both envelope alignment and phase adjustment. The key to realizing PDVM is resolving phase ambiguity. The traditional method for resolving ambiguity has very high requirements for the signal-to-noise ratio (SNR). This work resolves ambiguity by combining multiframe data, i.e., by resolving ambiguity of multiframe data simultaneously instead of resolving ambiguity of single-frame data independently and correcting the above ambiguity-resolving results using a minimum-entropy method. Therefore, phase ambiguity can be correctly resolved under a relatively low SNR. Experimental results of an ISAR imaging of an airplane show that the method proposed in this paper can obtain high-quality ISAR imagery, and can efficiently realize robust imaging under the conditions of low SNR. [ABSTRACT FROM AUTHOR]