Parametric study of sensor placement for vision-based relative navigation system of multiple spacecraft

In order to overcome the limited range of GPS-based techniques, vision-based relative navigation methods have recently emerged as alternative approaches for a high Earth orbit (HEO) or deep space missions. Therefore, various vision-based relative navigation systems use for proximity operations between two spacecraft. For the implementation of these systems, a sensor placement problem can occur on the exterior of spacecraft due to its limited space. To deal with the sensor placement, this paper proposes a novel methodology for a vision-based relative navigation based on multiple position sensitive diode (PSD) sensors and multiple infrared beacon modules. For the proposed method, an iterated parametric study is used based on the farthest point optimization (FPO) and a constrained extended Kalman filter (CEKF). Each algorithm is applied to set the location of the sensors and to estimate relative positions and attitudes according to each combination by the PSDs and beacons. After that, scores for the sensor placement are calculated with respect to parameters: the number of the PSDs, number of the beacons, and accuracy of relative estimates. Then, the best scoring candidate is determined for the sensor placement. Moreover, the results of the iterated estimation show that the accuracy improves dramatically, as the number of the PSDs increases from one to three.