The ability to produce high-resolution images of the Earth's surface from space has flourished in recent years with the continuous development and improvement of satellite-based imaging sensors. Earth-imaging satellites often rely on complex onboard navigation systems, with dependence on Global Positioning System (GPS) tracking and/or continuous post-capture georegistration, to accurately geolocate ground targets of interest to either commercial and military customers. Consequently, these satellite systems are often massive, expensive, and susceptible to poor or unavailable target tracking capabilities in GPS-denied environments. Previous research has demonstrated that a tightlycoupled image-aided inertial navigation system (INS), using existing onboard imaging sensors, can provide significant target tracking improvement over that of conventional navigation and tracking systems. Satellite-based image-aided navigation is explored as a means of autonomously tracking stationary ground targets by implementing feature detection and recognition algorithms to accurately predict a ground target's pixel location within subsequent satellite images. The development of a robust satellite-based image-aided INS model offers a convenient, low-cost, low-weight and highly accurate solution to the geolocation precision problem, without the need of human interaction or GPS dependency, while simultaneously providing redundant and sustainable satellite navigation capabilities.