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The guidance system in a spacecraft determines spacecraft attitude by matching an observed star field to a star catalog....An APS(active pixel sensor)-based system can reduce mass and power consumption and radiation effects compared to a CCD(charge-coupled device)-based system...This paper reports an APS (active pixel sensor) with locally variable times, achieved through individual pixel reset (IPR).
Previous studies revealed that satellites sensors with the best detection capability identify 25 %–40 % and 0 %–25 % fewer clouds below 0.5 and between 0.5–1.0 km, respectively, over the Arctic. Quantifying the impacts of cloud detection limitations on the radiation flux are critical especially over the Arctic Ocean considering the dramatic changes in Arctic sea ice. In this study, the proxies of the space-based radar, CloudSat, and lidar, CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations), cloud masks are derived based on simulated radar reflectivity with QuickBeam and cloud optical thickness using retrieved cloud properties from surface-based radar and lidar during the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment. Limitations in low-level cloud detection by the space-based active sensors, and the impact of these limitations on the radiation fluxes at the surface and the top of the atmosphere (TOA), are estimated with radiative transfer model Streamer. The results show that the combined CloudSat and CALIPSO product generally detects all clouds above 1 km, while detecting 25 % (9 %) fewer in absolute values below 600 m (600 m to 1 km) than surface observations. These detection limitations lead to uncertainties in the monthly mean cloud radiative forcing (CRF), with maximum absolute monthly mean values of 2.5 and 3.4 Wm –2 at the surface and TOA, respectively. Cloud information from only CALIPSO or CloudSat lead to larger cloud detection differences compared to the surface observations and larger CRF uncertainties with absolute monthly means larger than 10.0 Wm –2 at the surface and TOA. The uncertainties for individual cases are larger – up to 30 Wm –2 . These uncertainties need to be considered when radiation flux products from CloudSat and CALIPSO are used in climate and weather studies.
Satellite instruments have proven especially capable at monitoring the quantity of airborne particles in columns of atmosphere, globally. This chapter describes the principles of satellite measurements and retrieval algorithms, and surveys current instruments and their capabilities. We outline the issues associated with retrieval algorithms, such as surface characterization and aerosol proximity to clouds, and the challenges with interpretation of the results. The relationship between measured aerosol properties and climate-relevant aerosol properties simulated in models is outlined, as well as how measurements are used to evaluate models. Most space-based aerosol instruments are passive sensors that measure reflected sunlight at multiple wavelengths, some at multiple viewing angles. A few are active sensors that send out their own laser light and measure the returned signal. Except when clouds are present, the excess amount of light scattered back to space, beyond that expected from the surface and atmospheric gas, is attributed to aerosol. Satellite measurements are used in many ways in aerosol research. They often provide the only method for monitoring hazardous phenomena such as major wildfire and volcanic eruption plumes, especially in remote areas. Stable, long-term, near-global-scale satellite data records make it possible to identify regional and global aerosol trends. Aerosol radiative effects on climate can be quantified on a near-global scale and used to estimate the strength of aerosol–radiation and aerosol–cloud interactions as well as to evaluate climate model simulations of these interactions. Aerosol-type mapping from satellite imagery is helpful for source attribution, model validation, and to constrain particle light-absorption properties that are essential for radiative forcing calculations. The range of aerosol properties retrieved from satellite observations has grown considerably since the first global estimates of aerosol optical depth (τ a) over ocean were made in the late 1970s. Methods for retrieving particle size and light-absorption properties were explored in the 1990s using multispectral, multi-angle observations, and polarization in visible and near-infrared wavelengths. Sensitivity to particle light absorption, primarily from black or brown carbon content, improved with the inclusion of UV channels, and sensitivity to very thin aerosol layers in the upper troposphere and lower stratosphere was advanced with the use of limb-sounding instruments and active sensors. There are limitations to every measurement technique, including satellite aerosol remote sensing. For wide-swath, passive instruments, aerosol retrievals near clouds can present substantial challenges as far as 15 km away due to cloud-scattered light contaminating the signal. In nearly all cases, retrievals over bright snow and ice surfaces are precluded because surface reflectance uncertainties can overwhelm the aerosol signal. Similarly, meteorological cloud is identified and masked out where possible. Data from passive sensors also lack vertical resolution except those that view toward the limb or where multi-angle imagery is acquired over plumes from wildfires, erupting volcanoes, and wind-blown dust. Yet, passive sensors provide vastly more coverage than the active instruments that mitigate these issues. Particle microphysical information is qualitative from all remote sensing techniques, relying on proxies to infer particle composition, hygroscopicity, and the amount of light-absorbing material. Further, particles smaller than about 200 nm diameter cannot be distinguished from atmospheric gas molecules with remote sensing, which hinders studies of cloud condensation nuclei and their effects on clouds. Most satellite instruments dedicated to aerosol observations are in low-Earth, near-polar, sun-synchronous orbits, which means they cross the equator at the same local time each day. Most are set on cycles that repeat approximately every 16 days, which makes it difficult to monitor aerosol evolution locally. Geostationary satellites make it possible to observe changes occurring from minutes to hours over regions up to 8000 km in size, but lack coverage of high latitudes, and often provide more limited constraints on aerosol properties. Ground-truth data are vital for satellite aerosol-retrieval validation. The AErosol RObotic NETwork (AERONET) of sun photometers was created in 1993 and has become an established global network of over 350 instruments for validating satellite measurements. The network, as well as global networks of ground-based lidars, solar flux radiometers and other sun photometers, are widely used for evaluating global satellite retrievals and model simulations. NASA's Earth Observing System (EOS) program beginning in 1999 led to improvements in reliability, spatial resolution, and spectral resolution (and hence, to improved particle size discrimination and light absorption properties). Satellite payloads include advanced broad-swath and multi-angle imagers, along with the first space-based active sensor focused largely on long-term aerosol monitoring. Since about 2002, Europe's SENTINEL and operational meteorological satellite fleets are also providing sustained aerosol observations, with planned continuation until at least 2030. Satellite remote sensing instruments offer valuable data for evaluating aerosol representations in global climate models. They have been used to assess aerosol optical and physical properties, trends and distributions, and are applied increasingly as direct model constraints in data assimilation to create global aerosol reanalysis products. Aerosol optical depth is the most common quantity adopted for routine model evaluation, including multiwavelength data to loosely constrain particle-size distributions. These evaluations of multiple models have revealed general biases in their regional aerosol amounts and seasonal patterns of transport and removal. Although satellite measurements have near-global coverage, substantial errors can be introduced into the model observation comparison unless attention is paid to spatial and temporal collocation, cloud screening, subgrid-scale variability, and measurement uncertainties that vary with retrieval conditions.
The complementary metal-oxide-semiconductor (CMOS) active pixel sensor (APS) technology has been developed over the past three years by NASA at the Jet Propulsion Laboratory, and has reached a level of performance comparable to CCDs with greatly increased functionality but at a very reduced power level.
A compact VLSI neural computer integrated with an active pixel sensor has been under development to mimic what is inherent in biological visio systems.
Ongoing efforts at NASA’s Langley Research Center have produced a single passive sensor system for detecting ground objects and pinpointing their real-world location to a desired level of precision. The Langley center serves as a test range for unmanned aerial systems (UAS) and real-time knowledge about the location of people on campus is needed to inform least-risk UAS flight operations. The proposed system provides this knowledge through a camera combined with a convolutional neural network and an algorithm that projects an imaginary grid of square cells from the ground plane onto the perspective view of the camera. The position of detected objects on the camera’s projected grid determines their location in the real-world. The imaginary grid is easily mapped to a universal coordinate system, such as longitude and latitude, to provide both relative and absolute positional information of the detected objects. This simple system is shown to be accurate and effective, with decisive advantages over alternative multi-sensor and active sensor approaches. Extensions to the system are described to allow adaptation to a variety of other use cases.
The dosimetric response of an active pixel sensor is analyzed. heavy ions are seen to damage the pixel in much the same way as gamma radiation. The probability of a hot pixel is seen to exhibit behavior that is not typical with other microdose effects.
The possibility of utilizing low-orbit active microwave sensors in Search and Rescue (SAR) operations to detect and positionally locate scenes of distress is studied. The use of a scanning type system, i.e., rotating a highly directional antenna as opposed to more complex synthetic aperture type system is addressed. Targets for this system will be passive reflectors, not active transmitters like EPIRBs; reflectors which could be easily stowed, require little to no maintenance, could be quickly erected and can be more affordable than battery powered alternatives. Presented are parametric analyses to determine the required antenna gains and reflector areas for various frequencies and orbit altitudes. Also, major issues are raised as well as indicating areas requiring further study.
An active pixel cell includes electronic shuttering capability. The cell can be shuttered to prevent additional charge accumulation. One mode transfers the current charge to a storage node that is blocked against accumulation of optical radiation. The charge is sampled from a floating node. Since the charge is stored, the node can be sampled at the beginning and the end of every cycle. Another aspect allows charge to spill out of the well whenever the charge amount gets higher than some amount, thereby providing anti blooming.
This paper describes the technology, design features and reliability characterization methodology of a CMOS Active Pixel Sensor. Both overall chip reliability and pixel reliability are projected for the imagers.
A semiconductor imaging system preferably having an active pixel sensor array compatible with a CMOS fabrication process. Color-filtering elements such as polymer filters and wavelength-converting phosphors can be integrated with the image sensor.
A low power smart vision system based on a large format (currently 1K x 1K) active pixel sensor (APS) integrated with a programmable neural processor for fast vision applications is presented.
This paper will present the JPL-developed complementary metal- oxide-semiconductor (CMOS) active pixel sensor (APS) technology. The CMOS APS has achieved performance comparable to charge coupled devices, yet features ultra low power operation, random access readout, on-chip timing and control, and on-chip analog to digital conversion. Previously published open literature will be reviewed.
This paper presents heavy ion data on the single event transient (SET) response of a Photobit active pixel sensor (APS) four quadrant test chip with different radiation tolerant designs in a standard 0.35 micron CMOS process. The physical design techniques of enclosed geometry and P-channel guard rings are used to design the four N-type active photodiode pixels as described in a previous paper. Argon transient measurements on the 256 x 256 chip array as a function of incident angle show a significant variation in the amount of charge collected as well as the charge spreading dependent on the pixel type. The results are correlated with processing and design information provided by Photobit. In addition, there is a large degree of statistical variability between individual ion strikes. No latch-up is observed up to an LET of 106 MeV/mg/sq cm.
A low power smart vision system based on a large format (currently 1Kx1K) active pixel sensor (APS) integrated with a programmable neural processor for fast vision applications is presented.
This paper gives the status of theoretical and experimental efforts at JPL in the development of environmentally robust (radiation hard and radiation tolerant), ultra-low power, high performance CMOS active pixel sensor (APS) imagers for star tracker/imager applications.
A 128 x 128 element CMOS active pixel image sensor (APS) with on-chip timing, control, and.