Multi-Dimensional Parameter-Estimation Method for a Spatial Target Based on the Micro-Range Decomposition of a High-Resolution Range Profile

The high-precision estimation of multi-dimensional parameters for spatial targets based on high-resolution range profiles is crucial for target recognition. However, existing estimation methods face difficulties in resolving the strong coupling between the target shape and the micro-motion parameters, as well as in fully utilizing micro-motion information under complex modulation characteristics. To address these challenges, this paper proposes a multi-dimensional parameter-estimation method for spatial targets based on micro-range decomposition. A micro-range model of the target is first constructed, and the micro-range modulation characteristics are analyzed. Then, micro-range coefficients are selected based on their Cramér–Rao lower bound (CRLB), and the correlation between these coefficients and target parameters is exploited for scattering center matching. An optimization model is further built for multi-dimensional parameter estimation, enabling the accurate estimation of parameters such as precession frequency, precession angle, and structural dimensions under both single-view and multi-view conditions. The experimental results show that in the dual-view case, all parameters are estimated with relative errors (REs) below 1.15% and root mean square error (RMSE) values below 0.05. In the single-view case, key parameters are estimated with REs under 15%. Compared with conventional methods, the proposed method achieves lower RMSE and significantly improved robustness and stability. These results demonstrate the effectiveness and practical potential of the proposed method for spatial target parameter estimation.