Three-dimensional Reconstruction and Parameter Estimation of Smooth Symmetric Cone based on Multistatic Radar Adaptive Fusion

Aiming at the problem that single radar extracts micro-motion parameters inaccurately and has difficulty attaining 3D reconstruction, a novel 3D reconstruction algorithm based on the multi-aspect micro-motion information’s adaptive fusion is proposed to extract the micro-motion parameters in this paper. Firstly, radar network model and cone-shaped precession model are established and different-aspect conic node scattering point is matched and identified based on analyzing the relation of each scattering point’s micro-distance variation in detail. Then, regarding the observed information of every two radars as one solving unit and parameters from each unit are optimized by the weighed fusion array based on minimum variance estimating. Lastly, solving the coordinates of conic node and coning vector based on making use of optimizing parameters information. Moreover, cone attains 3D reconstruction based on the coordinates of conic node and coning vector. Simulation results prove that the proposed method can realize the extraction of high accuracy parameters and cone’s reconstruction availably.