Three-dimensional precession feature extraction of space targets

Precession is one of the most common kinds of micro-motions for space targets. Because the precession of a target consists of the synthesis of spinning motion and coning motion, the modulation characteristics of its radar returns are more complicated than those of a simple spinning target. This makes it very difficult to extract accurate micro-motion features and structure characteristics from the object's radar returns. In this paper, based on the distributed radar networks, we present an algorithm for extracting the three-dimensional (3-D) precession features of cone-shaped space targets. This algorithm takes the advantages of the multi-view of the distributed radar networks. In the paper, we first analyze the micro-Doppler (m-D) effect on range-slow-time plane induced by precession and then present the algorithm step by step, in which some relevant problems are discussed in detail and, meanwhile, the respective simulations are given. With aid of the proposed algorithm, some 3-D precession features and structure characteristics of a target, such as the 3-D coning vector, spinning period, precession period, precession angle and the radius of the cone bottom, can be extracted accurately. The length of the target can also be estimated. In the last section of the paper, we also give the discussion of the robustness of the proposed algorithm as well as the respective simulation results.