The Changchun Observatory of the National Astronomical Observatories, Chinese Academy of Sciences, and the Shanghai Astronomical Observatory are used to generate very short arc (VSA) angle observations of objects in low Earth orbit (LEO) and geostationary orbit (GEO) with their ground-based electrical–optical telescope arrays (EA), the Changchun EA and SAO FocusGEO, respectively. These observations are used in this paper. The range-searching (RS) algorithm for initial orbit determination (IOD) is improved through the multiple combinations of observations and the dynamic range-searching step length. Two different computation modes (the normal mode and the refining mode) of the IOD computation process are proposed. The geometrical method for the association is used. The IOD and association methods are extended to the real optical observations for both LEO and GEO objects. The results show that the IOD success rate of arcs from the LEO objects is about 91%, the error of the semimajor axis (SMA) of the initial orbital elements is less than 50 km, and the correlation accuracy rate is about 89%. The IOD success rate of arcs from the GEO objects is higher than 88%, and the correlation accuracy rate is greater than 87%. The recent COSMOS 1408 antisatellite test (ASAT) generated a large amount of debris. The algorithm of this paper and the observations of Changchun EA are used to initially identify new debris, possibly from the ASAT through initial orbit determination and track association. Finally, 64 suspected new pieces of debris can be found. The results show the effectiveness of the IOD and the correlation algorithm, as well as the potential application of the optical–electrical array in studying space events.

Wide-area space surveillance sensors are the backbone to cataloging of Earth orbiting objects. Their core capability should be to efficiently detect as many space objects as possible over a large space domain. As such, the question of how to quantitively evaluate the object detection performance of the sensors is critical. The evaluation is traditionally performed by means of infield static tests and out-field calibration satellite tests. However, this simplified method is flawed in terms of its representativeness in spatial-temporal coverage and object types, because space objects vary greatly in orbit type, size, and shape, and thus the evaluation results may be overoptimistic. This paper proposes a practically implementable procedure to quickly and reliably evaluate the object detection performance of space surveillance sensors in which a catalog containing a vast number of on-orbit objects is used as a reference. It first constructs a unified model to estimate the size of objects from its radar cross section (RCS) data, then it presents a hierarchy scheme to efficiently compute object visibility, and finally, it makes the sensor performance evaluation through a data point matching technique. Experiments with two simulated sensors demonstrate that the realized performance is always inferior to the designed one, and in some cases the difference is significant and concerning. The presented approach could be routinely applied to evaluate the performance of any operational surveillance sensors and provide insight on how the sensor performance could be improved through refined design, manufacture, and operation.