Geolocation Algorithm for Earth Observation Sensors Onboard the International Space Station

Abstract

As a near orbit space platform, the International Space Station (ISS) has been increasingly used for Earth observing applications. This paper presents a quaternion-based forward geolocation algorithm for Earth observing sensors onboard the ISS. The input parameters include the orbital state and attitude information of the ISS and the look vector of the sensor. The proposed algorithm agrees with the commercial navigation product, Satellite Tool Kit ® , within 0.5 m in ideal situations. The inherent uncertainties in ISS attitude and state determinations, and the International Space Station Agriculture Camera (ISSAC) tilting angle were estimated to introduce an error less than 800 m. However, the actual geolocation error evaluated using the images obtained by the ISSAC is roughly 4 km, much greater than the inherent uncertainty and mainly due to (a) delay caused by the Windows ® operating system in acquiring images, and (b) the misalignment of the ISSAC sensor coordinate system with the ISS body-fi xed coordinate system. A preliminary cal/val process using the Google Earth ™ as reference was performed to quantify these two errors, the correction of which improved the geolocation accuracy to 500 m, well within the inherent uncertainty.

Improving the Geolocation Algorithm for Sensors Onboard the ISS: Effect of Drift Angle

“…The drift angle can be calculated from the spacecraft orbital elements, i.e., the eccentricity, semi-major axis, inclination, longitude of the ascending node, argument of periapsis and mean anomaly at epoch [11,13,15]. However, to be consistent with the Dou et al [8] algorithm, which does not use these orbital element data, we propose a method to compute, by using the unit quaternion, the drift angle from the ISS state vectors and the positional information of the ground nadir point of the imagery. The drift angle effect also degrades the imaging quality due to the relative movement between the sensor and the target [16,17], which, however, is out of the scope of this study.…”

“…Dou et al [8] developed a forward quaternion-based geolocation algorithm to geo-reference images acquired by the Earth observing sensors onboard the ISS. The algorithm also accounts for the tilting capability that some sensors possess; for example, the ISSAC can tilt up to 30° in both port and starboard directions of the ISS to actively seek targets [10], and HICO can tilt up to 45° [5].…”

“…The CTRS is an Earth Centered Rotating (ECR) reference frame with its origin at the mass center of the Earth, its Z-axes aligning with the International Reference Pole (IRP) and X-axes with International Reference Meridian (IRM). In the CTRS reference frame, the geographic location of the pixel is sought as the intersection of the transformed look vector and the Earth surface, which is defined as an ideal Earth ellipsoid overlaid by a digital elevation model [8]. Figure 3 illustrates geometric relationship of a sensor onboard the ISS and its imaging objects on the ground in an Earth Centered Inertial (ECI) coordinate system (CS), which is fixed in space and does not rotate with the Earth.…”

“…To precisely geo-reference the Earth observing imagery acquired by the payload systems onboard the ISS, Dou et al developed a quaternion-based geolocation algorithm [8]. Applying the algorithm to the images acquired by the ISSAC [2,9] and comparing the results with Google Earth TM , they estimated that the algorithm has a geolocation accuracy of 190 ± 140 m with maximum errors less than 500 m in nadir points if the orientation of the sensor in the ISS body-fixed coordinate system was accounted for.…”

“…The objective of this paper is to investigate the cause of this rotational error and to provide a correction to further improve the Dou et al geolocation algorithm [8].…”