A self-learning refined model and tracking for near space hypersonic vehicle by space-based radar

The Near Space Hypersonic Vehicle (NSHV) features a unique design and propulsion system, achieving exceptional speed, range, and maneuverability, which challenge ground-based radars. Space-Based Radar (SBR) offers a breakthrough for tracking NSHV targets, with all-weather operation and freedom from Earth’s curvature, but faces complex coordinate transformations. Traditional models often overlook the NSHV’s dynamic gliding trajectory, especially the impact of hidden control variables on maneuvering, causing mismatches during rapid motion changes. This paper proposes a refined tracking model unified in the ECEF coordinate frame, incorporating model parameters that implicitly encode control laws, and presents an Expectation-Maximization Multi-swarm Cooperative Particle Swarm Optimization (EM-MCPSO) framework for both NSHV tracking and model parameter estimation to address this problem. To minimize conversion errors, a transformation matrix directly represented by the state in the Earth-Centered Earth-Fixed (ECEF) coordinate is derived. Then the hybrid aerodynamic acceleration coefficients are introduced to precisely describe the dynamic behaviors, formulating target tracking as a joint estimation problem of state and parameters within EM framework. Finally, a self-learning algorithm based on a master-slave structured PSO is proposed to solve the optimization of the conditional expectations of EM under strong nonlinearity, with a Proportional-Derivative (PD) controller accelerating convergence, and updating the population structure with historical data. Simulations of vertical gliding and horizontal maneuvers validate the algorithm’s effectiveness.