Characterization of Rotating Objects with Tomographic Reconstruction of Multi-Aspect Scattered Signals

The backscattered signals of objects under spinning motion or with rotating parts provide very rich information that can be used for classification tasks, parameter extraction, etc. Obtaining such information from noncooperative objects with an unknown target-aspect is often a complicated task with a monostatic configuration. A multistatic radar on the other hand, can exploit the spatial diversity to extract the information from the time–frequency representations obtained from multiple aspect-angles. In this paper, we propose a tomographic approach for characterizing spinning objects in terms of their shape, size, and rotation parameters using a narrow-band multistatic radar. A two-dimensional image is reconstructed after a full rotation period using tomographic methods that allows not only to estimate the shape of the target but also the rotation parameters and the dimensions of the object. This is done very efficiently by combining the tomographic images from different aspect-angles on the transformed log-polar space, instead of the time–frequency representations. Simulations and measurements were conducted for the proof of concept. The measurement results with a simple target and a continuous wave K-band radar show errors below 3 $^{\circ }$ for the orientation estimation and below 5% for the estimation of the object's diameter.