Research on Multistatic Radar Localization Methods for GEO Targets

This paper proposes a dual-iteration algorithm with orbital dynamics constraints to address the high-precision localization and velocity estimation of geostationary orbit (GEO) targets in multistatic radar systems. The proposed algorithm constructs a joint optimization framework for position and velocity by alternating iterative updates, dynamically decoupling parameter interdependencies, overcoming limitations of existing methods like multidimensional scaling (MDS) and two-step weighted least squares (TSWLS). Key innovations include: (1) a regularization term incorporating orbital energy conservation, angular momentum consistency, and perturbation matching to enforce kinematic constraints; (2) a perturbation compensation operator mitigating J2 gravitational and solar radiation pressure effects; (3) adaptive weight matrices to suppress non-Gaussian noise. Simulations demonstrate superior performance over TSWLS and MDS, particularly under low signal-to-noise ratios, with reduced root-mean-square error (RMSE) trends as noise increases. The dual-iteration mechanism eliminates error propagation in TSWLS and avoids diagonal loading dependency in MDS algorithm, while orbital state projection refines velocity estimates. This work bridges the gap in GEO target tracking, offering a robust solution for space situational awareness under strong perturbations and weak-signal conditions.