Light curve attitude estimation using particle swarm optimizers

Knowledge of the attitude of a space object is useful in space situational awareness for independently evaluating a satellite’s health and characterizing unknown objects. In cases where only non-resolved optical observations are available, the object’s attitude may be estimated using a time sequence of brightness observations, also known as the light curve. This attitude estimation problem is plagued with multiple difficulties: even in the absence of noise and when all other relevant factors are perfectly known, the non-uniqueness of the problem means that multiple attitude time histories may fit the light curve equally well. In addition, there is often insufficient information about the object to generate an initial state guess for an estimator. This paper presents a method that estimates an observed object’s attitude and angular velocity while accounting for ambiguities and without needing any initial state guess. The only inputs are the light curve, the object’s albedo shape, and the object’s position relative to the Sun and the observer. The ability of the estimator to resolve attitude time histories is demonstrated using simulated light curves by comparing state estimates against known true states.