Robust Estimation And Adaptive Guidance For Multiple Uavs\u27 Cooperation

In this paper, an innovative cooperative navigation method is proposed for multiple Unmanned Air Vehicles (UAVs) based on online target position measurements. These noisy position measurement signals are used to estimate the target\u27s velocity for non-maneuvering targets or the target\u27s velocity and acceleration for maneuvering targets. The estimator\u27s tracking capability is physically constrained due to the target\u27s kinematic limitations and therefore is potentially improvable by designing a higher performance estimator. An H-infinity filter is implemented to increase the robustness of the estimation accuracy. The performance of the robust estimator is compared to a Kalman filter and the results illustrate more precise estimation of the target\u27s motion in compensating for surrounding noises and disturbances. Furthermore, an adaptive guidance algorithm, based on the seeker\u27s field-of-view and linear region, is used to deliver the pursuer to the maneuvering target. The initial guidance algorithm utilizes the velocity pursuit guidance law because of its insensitivity to target motion; while the terminal guidance algorithm leverages the acceleration estimates (from the H-infinity filter) to augment the proportional navigation guidance law for increased accuracy in engaging maneuvering targets. The main objective of this work is to develop a robust estimator/tracker and an adaptive guidance algorithm which are directly applicable UAVs

Space-based Maneuver Detection and Characterization using Multiple Model Adaptive Estimation

An increasingly congested space environment requires real-time and dynamic space situational awareness (SSA) on both domestic and foreign space objects in Earth orbits. Current statistical orbit determination (SOD) techniques are able to estimate and track trajectories for cooperative spacecraft. However, a non-cooperative spacecraft performing unknown maneuvers at unknown times can lead to unexpected changes in the underlying dynamics of classical filtering techniques. Adaptive estimation techniques can be utilized to build a bank of recursive estimators with different hypotheses on a system\u27s dynamics. The current study assesses the use of a multiple model adaptive estimation (MMAE) technique for detecting and characterizing noncooperative spacecraft maneuvers using space-based sensors for spacecraft in close proximity. A series of classical and variable state multiple model frameworks are implemented, tested, and analyzed through maneuver detection scenarios using relative spacecraft orbit dynamics. Variable levels of noise, data availability, and target thrust profiles are used to demonstrate and quantify the performance of the MMAE algorithm using Monte Carlo methods. The current research demonstrates that adaptive estimation techniques are able to handle unknown changes in the dynamics while keeping comparable errors with respect to other classical estimation methods