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This paper discusses common approaches to CMOS APS technology, as well as specific results on the five-wire programmable digital camera-on-a-chip developed at JPL. The paper also reports recent research in the design, operation, and performance of APS imagers for several imager applications.
NASA's Marshall Space Flight Center was the driving force behind the development of the Advanced Video Guidance Sensor, an active sensor system that provides near-range sensor data as part of an automatic rendezvous and docking system. The sensor determines the relative positions and attitudes between the active sensor and the passive target at ranges up to 300 meters. The VGS uses laser diodes to illuminate retro-reflectors in the target, a solid-state camera to detect the return from the target, and image capture electronics and a digital signal processor to convert the video information into the relative positions and attitudes. This development effort has required a great deal of testing of various sorts at every phase of development, and some of the test efforts, predictions, and results will be laid out in this paper.
NASA's Marshall Space Flight Center was the driving force behind the development of the Advanced Video Guidance Sensor, an active sensor system that provides near-range sensor data as part of an automatic rendezvous and docking system. The sensor determines the relative positions and attitudes between the active sensor and the passive target at ranges up to 300 meters. The AVGS uses laser diodes to illuminate retro-reflectors in the target, a solid-state camera to detect the return from the target, and image capture electronics and a digital signal processor to convert the video information into the relative positions and attitudes. The AVGS will fly as part of the Demonstration of Autonomous Rendezvous Technologies (DART) in October, 2004. This development effort has required a great deal of testing of various sorts at every phase of development. Some of the test efforts included optical characterization of performance with the intended target, thermal vacuum testing, performance tests in long range vacuum facilities, EMI/EMC tests, and performance testing in dynamic situations. The sensor has been shown to track a target at ranges of up to 300 meters, both in vacuum and ambient conditions, to survive and operate during the thermal vacuum cycling specific to the DART mission, to handle EM1 well, and to perform well in dynamic situations.
Radiation hardened/tolerant Active Pixel Sensor CMOS imagers that can be adaptively windowed with low power, on-chip control, timing, digital output and provide data-channel efficient on-chip compression, high bandwidth optical communications links are being designed and investigated to reduce size, weight and cost for common optics/hybrid architectures.
The Advanced Video Guidance Sensor (AVGS), an active sensor system that provides near-range 6-degree-of-freedom sensor data, has been developed as part of an automatic rendezvous and docking system for the Demonstration of Autonomous Rendezvous Technology (DART). The sensor determines the relative positions and attitudes between the active sensor and the passive target at ranges up to 300 meters. The AVGS uses laser diodes to illuminate retro-reflectors in the target, a solid-state imager to detect the light returned from the target, and image capture electronics and a digital signal processor to convert the video information into the relative positions and attitudes. The development of the sensor, through initial prototypes, final prototypes, and three flight units, has required a great deal of testing at every phase, and the different types of testing, their effectiveness, and their results, are presented in this paper, focusing on the testing of the flight units. Testing has improved the sensor's performance.
A CMOS imaging sensor is described that uses active pixel sensor (APS) technology which permits the integration of the detector array with on-chip timing, control and signal chain electronics. This sensor has been used to implement a CMOS APS camera-on-a-chip.
A newly fabricated Monolithic InGaAs Active Pixel Image Sensor is presented, and its readout characteristics are described.
To ensure proper sensor operations, it is necessary to understand the situation of potential interference to sensors due to active equipment sharing common frequency bands as well as equipment occupying adjacent bands. The feasibility of sharing common frequency bands between passive sensors and other active services was analyzed. Potential interference to sensors due to equipment in bands adjacent to sensor frequency bands is examined and criteria to avoid interference is developed.
Active pixel sensors for a high quality imager are fabricated using a silicon-on-insulator (SOI) process by integrating the photodetectors on the SOI substrate and forming pixel readout transistors on the SOI thin-film. The technique can include forming silicon islands on a buried insulator layer disposed on a silicon substrate and selectively etching away the buried insulator layer over a region of the substrate to define a photodetector area. Dopants of a first conductivity type are implanted to form a signal node in the photodetector area and to form simultaneously drain/source regions for a first transistor in at least a first one of the silicon islands. Dopants of a second conductivity type are implanted to form drain/source regions for a second transistor in at least a second one of the silicon islands. Isolation rings around the photodetector also can be formed when dopants of the second conductivity type are implanted. Interconnections among the transistors and the photodetector are provided to allow signals sensed by the photodetector to be read out via the transistors formed on the silicon islands.
Active pixel sensors for a high quality imager are fabricated using a silicon-on-insulator (SOI) process by integrating the photodetectors on the SOI substrate and forming pixel readout transistors on the SOI thin-film. The technique can include forming silicon islands on a buried insulator layer disposed on a silicon substrate and selectively etching away the buried insulator layer over a region of the substrate to define a photodetector area. Dopants of a first conductivity type are implanted to form a signal node in the photodetector area and to form simultaneously drain/source regions for a first transistor in at least a first one of the silicon islands. Dopants of a second conductivity type are implanted to form drain/source regions for a second transistor in at least a second one of the silicon islands. Isolation rings around the photodetector also can be formed when dopants of the second conductivity type are implanted. Interconnections among the transistors and the photodetector are provided to allow signals sensed by the photodetector to be read out via the transistors formed on the silicon islands.
An architecture for a proposed active-pixel sensor (APS) and a design to implement the architecture in a complementary metal oxide semiconductor (CMOS) very-large-scale integrated (VLSI) circuit provide for some advanced features that are expected to be especially desirable for tracking pointlike features of stars. The architecture would also make this APS suitable for robotic- vision and general pointing and tracking applications. CMOS imagers in general are well suited for pointing and tracking because they can be configured for random access to selected pixels and to provide readout from windows of interest within their fields of view. However, until now, the architectures of CMOS imagers have not supported multiwindow operation or low-noise data collection. Moreover, smearing and motion artifacts in collected images have made prior CMOS imagers unsuitable for tracking applications. The proposed CMOS imager (see figure) would include an array of 1,024 by 1,024 pixels containing high-performance photodiode-based APS circuitry. The pixel pitch would be 9 m. The operations of the pixel circuits would be sequenced and otherwise controlled by an on-chip timing and control block, which would enable the collection of image data, during a single frame period, from either the full frame (that is, all 1,024 ~ 1,024 pixels) or from within as many as 8 different arbitrarily placed windows as large as 8 by 8 pixels each. A typical prior CMOS APS operates in a row-at-a-time ( grolling-shutter h) readout mode, which gives rise to exposure skew. In contrast, the proposed APS would operate in a sample-first/readlater mode, suppressing rolling-shutter effects. In this mode, the analog readout signals from the pixels corresponding to the windows of the interest (which windows, in the star-tracking application, would presumably contain guide stars) would be sampled rapidly by routing them through a programmable diagonal switch array to an on-chip parallel analog memory array. The diagonal-switch and memory addresses would be generated by the on-chip controller. The memory array would be large enough to hold differential signals acquired from all 8 windows during a frame period. Following the rapid sampling from all the windows, the contents of the memory array would be read out sequentially by use of a capacitive transimpedance amplifier (CTIA) at a maximum data rate of 10 MHz. This data rate is compatible with an update rate of almost 10 Hz, even in full-frame operation
A new, photon-sensitive, imaging array, the active pixel sensor (APS) has emerged as a competitor to the CCD imager for use in star and target trackers.
Radiative transfer models are extensively used for the assimilation of satellite observations into NWP models as well as retrieving geophysical products from satellite measurements. CRTM is a community model developed by NOAA JCSDA and widely used for different purposes requiring RT calculations. CRTM requires bulk scattering lookup tables in order to perform all-sky RT calculations. However, the current CRTM lookup tables for microwave frequencies were generated based on the Mie theory by assuming spherical frozen particles. The scattering lookup tables generated using the DDA technique has shown to largely improve the RT scattering calculations in the MW region. This presentation targets (i) the implementation and validation of a DDA database that was originally developed for the ARTS RT model into CRTM, and (ii) developing the CRTM active sensor module that takes advantage of the backscattering coefficients computed using the DDA method. The DDA database only provides single scattering properties of different habits, while CRTM requires bulk scattering properties. The CRTM cloud coefficients were previously generated based on the effective radius for representing the size of the particles. However, effective radius is neither measurable nor provided by the NWP models, thus need to be estimated from other geophysical variables such as water content. Therefore, in addition to calculating the CRTM bulk scattering properties from the DDA single scattering database, the CRTM was also largely modified to use cloud water content (kg.m-3), instead of effective radius, for performing the interpolation over size/mass of the particles. CRTM already requires water content as input, thus no extra variables are required for performing scattering calculations using the new ARTS DDA database. The CRTM scattering modules search for effective radius in cloud coefficient files and will use the cloud water content if the effective radius dimension is not found in the cloud coefficient files. Figure 1 shows the CRTM simulated brightness temperatures computed using different cloud coefficients versus ATMS observed values over Hurricane Irma on September 7, 2017 at 18:00 UTC. We used all the cloud water content values included in ERA5 with default CRTM/DDA habits for water, rain, snow, ice, hail, and graupel. ERA5 does not provide separate water content values for ice, hail, and graupel, thus the ice water content values were divided between ice, hail, and graupel clouds similar to what was explained in the previous section. In channels with a frequency lower than 90 GHz, emission from water and rain clouds can compensate for cloud scattering so that cloud contaminated Tbs are larger than corresponding clear sky Tbs. The DDA simulations for channels 1-7 largely perform better than the Mie simulations. The DDA simulations show a mix of small negative and positive simulated minus observed values, while the Mie results show large negative biases. The weighting functions for some of the ATMS temperature sounding channels (channels 9-15) peak mostly above the clouds, therefore the measured Tbs become less sensitive to clouds so that the results of both Mie and DDA become very similar. The Mie lookup tables generate excessive scattering for channel 16, but not enough scattering for the water vapor channels. In the specific case of Hurricane Maria, the DDA lookup tables do not generate enough scattering for channel 16, but the DDA results are much more consistent with observations for water vapor channels than for channel 16. It should be noted that the results may vary if we use other habits to represent snow, hail, and graupel in the DDA simulations. Although these results clearly show the advantage of the DDA database over the Mie dataset, different error sources such as error in the observations, displacement of clouds in the ERA5 reanalysis, and also lack of convective clouds or in general errors in the input atmospheric and cloud profiles contribute to the differences between the simulated and observed values. Aside from the improvements in the simulations, a major advantage of the new dataset is a large number of habits that can be used to tune the data assimilation systems to perform well in different weather conditions.
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).
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.