A Symmetry-Based Unscented Particle Filter for Rapid State Estimation for SAL Guided Vehicles

The state estimation problem for vehicles with highly uncertain initial conditions and limited, varying sensors is crucial for both aircraft and spacecraft navigation. This work introduces a locally linearized particle filter based on a quaternion-adapted unscented Kalman filter to estimate the state of a free-fall laser-guided bomb with minimal sensors and uncertain initial conditions. The available sensors include accelerometers, gyroscopes, a barometric altimeter, and a semiactive laser receiver that activates only when the target is close and within line-of-sight. Assuming no communication between the carrier aircraft and the bomb (so that the aircraft cannot feed the bomb its launch position and velocity), the algorithm exploits the problem's symmetry to rapidly reconstruct the relative position, velocity, and attitude of the target, even with uncertain initial conditions and insufficient sensor data. In addition, the filter initiates an identification algorithm to estimate the ballistic coefficient, which predicts the miss distance. The proposed algorithm shows promising results in Monte Carlo simulations, quickly converging to an accurate trajectory estimate and providing a high quality aerodynamic model and future trajectory predictions.