Automatic Detection Algorithm for Small Moving Objects

Self Cite

We have examined the possibilities of a ground-based optical observation system for monitoring LEO objects. Simulations and a test observation showed that two longitudinally separate observation sites with arrays of optical sensors can detect many LEO objects 30 cm in size and precisely determine their orbit. The proposed optical survey system may complement or replace the current radar observation system for LEO objects monitoring in the near future.

Section: Discussionmentioning

confidence: 99%

Investigation of a Ground-based Optical Observation System for LEO Objects

Yanagisawa

Kurosaki

Oda

2014

Self Cite

“…Details of the stacking method are shown in other papers. [9][10][11] The only weakness of the stacking method is the time required to detect an unseen object whose movement is not known, because a range of likely paths must be assumed and checked. For example, the analysis time for 65,536 processing iterations of 32 1,024×1,024-pixel frames, which are intended to detect objects moving within a 256×256-pixel area, is about 280 hours using a normal desktop computer, which is not really practical.…”

Section: Fpga Based Analysis Systemmentioning

confidence: 99%

Detection of LEO Objects Using CMOS Sensor

Yanagisawa¹,

Kurosaki²,

Oda

2016

Self Cite

We succeeded in detecting 10 cm LEO objects at 1000 km altitude with a CMOS sensor installed to the 18cm telescope by using fast frame rate of the CMOS sensor and FPGA-based image-processing technique. The LEO survey system using numerous sets of the CMOS sensor will contribute the monitoring LEO environments which is currently done with the space surveillance network of the United States and solving the space debris problem in the future.

“…We have been developing this method, investigating its effectiveness and trying to utilize it for actual GEO debris observation since 2000 [5][6][7] . In the field of astronomy, a similar "Shift-and-Add" technique developed by Yamamoto et al 8) have been used to detect unknown trans-Neptune objects since late 1990s.…”

Section: Principlementioning

confidence: 99%

Detection of Faint GEO Objects Using JAXA’s Fast Analysis Methods

Yanagisawa

Kurosaki

2012

Self Cite

JAXA is developing analysis methods which can detect faint geostationary (GEO) objects that are not in the catalog provided by U.S. The stacking method, which uses numerous CCD frames to detect objects below the background noise level, has been developed and has been shown to work well, but has the drawback that detecting objects whose movements are unknown is extremely time-consuming. To overcome this, a new algorithm is developed which uses binarization of CCD images and calculates sum values instead of medians. When implemented in a field programmable gate array (FPGA), the algorithm reduces the analysis time by a factor of a thousand over the original stacking method, which enables us to analyze one night's data before the following night's observation. Another fast analysis method is a line-identifying technique which finds straight-line candidates in time-sequential CCD frames and can also detect faint GEO objects. Although the stacking method can detect fainter objects, the line-identifying technique has much shorter analysis time and can detect 40 cm-size objects in GEO using a 35 cm telescope. These two analysis methods may contribute to detecting many un-cataloged objects in the near future.