The docking ring is a typical structure on space targets, and the spatial circle feature extracted from it is an important reference mark for non-cooperative space target vision measurement. However, there are two problems in the pose calculation process: the docking ring pose calculation result of the monocular camera is ambiguous, and the roll angle information cannot be obtained when the pose is solved. In this paper, a non-cooperative space target binocular vision 6- DOF pose measurement method based on docking ring feature is proposed. The binocular vision measurement system is used to solve the ambiguity problem of the docking ring calculation result, and the target coordinate system is constructed by selecting reference feature point outside the docking ring to realize 6-DOF pose measurement. The accuracy and stability of the method are verified by digital simulation experiments, and the results indicate that the method still can calculate stable pose when the pixel position error is 4 pixels. In the scene simulation experiments, the absolute error of vertical optical axis position measurement is less than 1.6 mm, the relative error of parallel optical axis position measurement is less than 0.7%, and the absolute error of attitude measurement is less than 0.2 deg, which can meet the requirements of space navigation mission.
The metal-semiconductor-metal (MSM) structured ZnO photodetectors with same electrode spacings are made by radio frequency magnetron sputtering. A study of the thermal annealing effects on photodetectors with sequential annealing temperature (300, 400, 500 and 600 ℃).The responsivity of the photodetector was enlarged greatly after annealing the MSM device. Meanwhile, the enhancement in the dark current that resulted from the experiment was accompanied by the increasing annealing temperature. These results demonstrate that a simple route to improve the responsivities of photodetectors can be realized easily by annealing the devices.
A space-based infrared camera was launched to collect atmospheric radiation data. In order to investigate its performance quantitatively both under pre-launch and post-launch conditions, a practical estimation model of radiometric calibration precision was proposed that only depended on the measured image data from ground and on-orbit blackbody-based calibration tests. The model treated the calibration error as a consequence of two independent factors. One was introduced by using the calibration equation to represent the relationship between the object apparent radiance and the camera digital response, and the other was the measurement uncertainty when imaging a target with known constant emission. Distribution maps of the errors for the focal plane array were constructed by means of estimating the calibration error pixel-wisely. Results show that the camera’s performance after launch is slightly worse than that before launch. The pixels with calibration errors more than 10% only account for about 5% for this camera, and they generally locate in the edge of the focal plane. The maps will be helpful in weighing the validity of sampled data at the pixel level.
Scattering phase function on horizontally oriented ice particles near the specular reflective direction is analytically modeled using a mixed method combining direct reflection and Fraunhofer diffraction components, where particles are simply treated as circular facets and the effect of fluttering is introduced under the assumption of Gauss distribution. The obtained model expression reveals that the essence of far-field scattering around specular direction is the diffraction pattern modulated by fluttered geometric reflection. Four groups of experiments are designed to validate this model at different wavelengths and incidence angles, and the calculated phase functions present good agreement both in distributions and peak values with that of T-matrix method in conjunction with a Monte Carlo stochastic process.
Proc. SPIE. 7658, 5th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronic Materials and Devices for Detector, Imager, Display, and Energy Conversion Technology
KEYWORDS: Staring arrays, Infrared sensors, Infrared imaging, Statistical analysis, Detection and tracking algorithms, Data modeling, Visualization, Digital filtering, Image filtering, Data analysis
IR focal plane arrays typically contain bad pixels. Bad pixels have to be corrected because those can significantly impair
the performance of target-detection algorithms. On the other hand, particularly as an aid to visual interpretation, it is
desirable to replace the bad pixels. IR image contains spatial information and is correlative in spatial domain. In spatial
statistics the semivariogram is an important function that relates semivariance to sampling lag. This function can
characterize the spatial dependence of each point on its neighbor and provide a concise and unbiased description of the
scale and pattern of spatial variability. One of the main reasons for deriving semivariogram is to use it in the process of
estimation. Kriging is an interpolation and estimation technique that considers both the distance and the degree of
variation between known data points when estimating values in unknown areas. In this paper a new technique based on
spatial statistics is developed for bad pixel replacement. The main objective of the technique is to replace bad pixels
through Kriging estimation. Theory analysis and experiments show that the method is reasonable and efficient.
Proc. SPIE. 6595, Fundamental Problems of Optoelectronics and Microelectronics III
KEYWORDS: Signal to noise ratio, Stars, Detection and tracking algorithms, Data modeling, Electrons, Interference (communication), Linear filtering, Surveillance, Charge-coupled devices, Signal detection
Dim object detection is the key technology to space objects surveillance. The space detection data model (SDDM) and
space dim moving object detection algorithm (SDMODA) are investigated systematically in this paper. SDDM is set up,
which consists of the circuit Gaussian noise, dark current Poisson noise, background light Poisson noise, non-moving
objects noise and moving object signal sub-model. By digital simulation, time sequence image data of each CCD pixel is
built to input SDMODA as raw data. SDMODA computes maximum value projection, pixel average and pixel standard
deviation in each CCD pixel of every time sequence data. By subtracting pixel average from maximum value projection
and then divided by pixel standard deviation in each pixel, SDMODA produces a new standard frame. The standard
frame is processed to find candidate object streaks by threshold filter, which can eliminate non-moving objects and
suppress the circuit Gaussian noise, dark current Poisson noise and background light Poisson noise. Since the
invariability of object streak in streak angle and streak length, SDMODA can eliminate false streaks and find true object
streak. By simulation experiment, SDDM can produce time sequence data with different signal-to-noise ratio and
SDMODA can detect object streak with signal-to-noise ratio of 3.5dB.