EVALUATION OF THE INFORMATION CONTENT OF OBSERVATIONS WITH APPLICATION TO SENSOR MANAGEMENT FOR ORBIT DETERMINATION

Information regarding the state of a space object (SO) is typically inferred via measurement systems such as optical telescopes or radar systems. As the measurement systems become more advanced, a greater number of SOs can be detected which leads to an ever-increasing number of SOs that need to be uniquely identified. Given the lack of ubiquitous observations, scarce sensor resources must be optimally utilized, i.e. so that the telescope or radar systems are not tasked to acquire redundant or useless information. Additionally, in the Situation where multiple systems can observe the same object, the observation geometry needs to be accounted for so that the sensor which can provide the most information is employed, enabling other sensors to dedicate their time to observing other objects. This leads to a need for methods which evaluate the information gain (ambiguity reduction) that can be expected from any given sensor observing any given SO. To address this need, this work examines several information-theoretic approaches to determining the information content of observations and compares the different developed methods in the context of assessing the measurement utility and observation scheduling for orbit determination problems.