scholarly journals Advanced sun-sensor processing and design for super-resolution performance

2021 ◽  
Author(s):  
Enright John
Keyword(s):  
Super Resolution ◽  
Noise Model ◽  
Algorithm Performance ◽  
Sun Sensor ◽  
Subpixel Accuracy ◽  
Sensor Processing ◽  
Optimal Illumination ◽  
The Cost ◽  
Parametric Algorithms ◽  
Test Regime

The performance of conventional and parametric super-resolution algorithms for estimating sun position in a spacecraft sun-sensor was analyzed. Widely employed in other applications, parametric algorithms were examined to evaluate increase in system performance without affecting the cost of the sensor system. Using a simplified model of detector illumination simulations provided quantitative comparisons of algorithm performance. Simple sensor re-design was examined by using genetic algorithms as a heuristic to optimize the illumination pattern for a single axis digital sun-sensor. Findings show that, multiple narrow peak patterns provide subpixel accuracy in resolving the sun-angle. The optimal illumination pattern can be implemented by fabricating a replacement aperture mask for the sensor and this change can be made at a minimal cost. The super-resolution algorithms were tested with a component noise model and image degradation due to Earth albedo effects were examined. Parametric algorithms display very good performance throughout the test regime. The improvements are substantial enough to validate this approach worthy of future study.

scholarly journals Advanced sun-sensor processing and design for super-resolution performance

2021 ◽  
Author(s):  
Enright John
Keyword(s):  
Super Resolution ◽  
Noise Model ◽  
Algorithm Performance ◽  
Sun Sensor ◽  
Subpixel Accuracy ◽  
Sensor Processing ◽  
Optimal Illumination ◽  
The Cost ◽  
Parametric Algorithms ◽  
Test Regime

The performance of conventional and parametric super-resolution algorithms for estimating sun position in a spacecraft sun-sensor was analyzed. Widely employed in other applications, parametric algorithms were examined to evaluate increase in system performance without affecting the cost of the sensor system. Using a simplified model of detector illumination simulations provided quantitative comparisons of algorithm performance. Simple sensor re-design was examined by using genetic algorithms as a heuristic to optimize the illumination pattern for a single axis digital sun-sensor. Findings show that, multiple narrow peak patterns provide subpixel accuracy in resolving the sun-angle. The optimal illumination pattern can be implemented by fabricating a replacement aperture mask for the sensor and this change can be made at a minimal cost. The super-resolution algorithms were tested with a component noise model and image degradation due to Earth albedo effects were examined. Parametric algorithms display very good performance throughout the test regime. The improvements are substantial enough to validate this approach worthy of future study.

The role of prior knowledge in coherent image processing

1988 ◽  
Vol 324 (1579) ◽  
pp. 397-407 ◽  
Keyword(s):  
Prior Knowledge ◽  
Super Resolution ◽  
Image Data ◽  
Noise Model ◽  
Sar Images ◽  
Coherent Image ◽  
Background Clutter ◽  
Value Decomposition ◽  
Target Characteristics

Models that encode prior knowledge about a scene provide a means for interpreting image data from that scene in more detail than would otherwise be so. Information about both background clutter and target characteristics should be included in this prior knowledge. We demonstrate the use of a generalized noise model to represent a variety of naturally occurring random terrain clutter textures observed in high-resolution synthetic aperture radar (SAR) images. In addition a similar approach is adopted for the simulation of such textures. Having established the background properties we next introduce prior knowledge about any target within the scene and exploit this in achieving a cross-section reconstruction having improved resolution compared with the original image. Examples of such a super-resolution method based on singular value decomposition are demonstrated and the limits of the technique are indicated.

scholarly journals Image Processing Techniques For Improved Sun Sensor Performance

2021 ◽  
Author(s):  
Christopher. Li
Keyword(s):  
Random Noise ◽  
Super Resolution ◽  
Least Square ◽  
Bias Error ◽  
Calibration Data ◽  
Small Motion ◽  
Sun Sensor ◽  
Improved Performance ◽  
Relative Measurements ◽  
Processing Techniques

The behaviour of digital sun-sensors and associated super-resolution algorithms was explored. Using calibration data, a method was proposed to model the peak width of peaks across the image array. Using this with the non-linear least square algorithm gave improved performance across the field-of-view. A test was proposed that would measure precision for small sensor motions. Also, a method of accounting for local bias error was given. The small motion test defined limits at which the sensor detects motion, and the precision test gave metrics to measure how well the sensor renders motion. Finally, an extended kalman filter was developed that used sun-vector measurements, in addition to a new relative measurement. This was tested using a well-defined sensor as well as a generic sensor for which few error data were known. Results indicate that relative measurements only improve performance if random noise is low.

Implementation and Realisation of Micro Digital Sun Sensor Processing in FPGA

2019 ◽  
Vol 29 (S1) ◽  
pp. 71-85
Author(s):  
Tarun Kohli ◽  
Amit Maji ◽  
Y. VIJAYA ◽  
H.S RAVINDRA
Keyword(s):  
Sun Sensor ◽  
Sensor Processing

scholarly journals MODELING OF THE ELECTROMAGNETIC FIELD OF THE OPTICAL SPECTRUM AT THE LEAVE OF THE PLANT

2020 ◽  
pp. 129-134
Author(s):  
Leonid Chervinsky ◽  
Tetiana Knizhka ◽  
Oleksii Romanenko
Keyword(s):  
Electromagnetic Field ◽  
Plant Development ◽  
Optical Spectrum ◽  
State Level ◽  
Subsequent Increase ◽  
Leaf Formation ◽  
Automatic Maintenance ◽  
Effective Level ◽  
Optimal Illumination ◽  
The Cost

Low levels of natural light in greenhouses and a short winter day require additional irradiation, and artificial maintenance of temperature and humidity, due to the significant cost of non-renewable energy. It is known that about half of the cost of production is accounted for by electricity. Therefore, the state level is gaining issues of conservation of electricity spent on lighting and irradiation of plants. One of the ways to reduce the cost of electricity for light crops is to improve the methods of calculating photosynthetic irradiation, followed by automatic maintenance of the effective level of irradiation. One of the ways to reduce energy consumption for photoculture of plants in protected ground structures and increase their productivity is to improve methods for determining and maintaining optimal illumination values with radiation of a given range, which will provide the most effective level of photosynthetic process of plant development with a subsequent increase in its productivity. In the given article, the modeling of the electromagnetic field of the optical spectrum at the level of plant leaf formation, which takes into account the scattered radiation and reflected by the surfaces of the walls and ceiling, is carried out. The proposed method increases the accuracy of determining the actual value of irradiance by taking into account the features of the brightness of the coating on the walls and ceiling and their reflection coefficient as a function of spatial coordinates. An example is given of using this method to determine and automatically maintain the actual photosynthetic irradiation in accordance with the standard value of light intensity in the production room of the greenhouse, which ensures effective plant development and, accordingly, maximum productivity.

scholarly journals Advances in Artificial Intelligence for the Underwater Domain

2019 ◽  
Vol 53 (5) ◽  
pp. 68-74
Author(s):  
Michael B. Gratton
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Systems ◽  
Traditional Approach ◽  
Autonomous Underwater Vehicles ◽  
Autonomous Systems ◽  
Software Systems ◽  
Underwater Robotics ◽  
Technical Advances ◽  
Sensor Processing ◽  
The Cost

AbstractWhere most so-called autonomous underwater vehicles are automated, they have limited autonomy in the traditional sense of being free to choose new courses of action. Onboard software systems have a narrow range of sensor processing and mission replanning capabilities due to the expense and the complexity of these operations, as well as a lack of operator trust in the validity of plans that operators cannot inspect before execution. The advent of machine learning and the maturation of new ideas in automated planning will reduce the cost and difficulty of fielding more autonomous systems, attacking the first difficulty. The second difficulty—that of trust—is severe for underwater robotics, where the more traditional approach of intelligent systems acting as operator aids is made difficult by the physics. Increasing trust will require technical advances in assured autonomy that are only just beginning. We survey the history of artificial intelligence (AI) for robotics, highlighting important developments that will influence future systems.

Sign in / Sign up

Export Citation Format

Share Document