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New generations of defense imaging systems, using sophisticated sensor technologies, generate invaluable information for modern warfare. Hyperspectral imaging (HSI) and laser radar (LADAR) will augment, but not replace, the electro-optic infrared (EO/IR) and synthetic aperture radar (SAR) sensors currently used today. The information generated by combining input from these sensors is powerful. For example, an infrared image overlaid on a radar image of a parking lot can determine which vehicles were recently used (still hot).
However, the timely use of that valuable information is impacted by bandwidth limitations in the data links that provide the transmission backbone from a sensor platform to a ground station. Current data links cannot transmit the full data streams generated by advanced sensors. Improvements in data communications will increase available bandwidth, but systems will still be overwhelmed by ever-larger information data streams from new generations of sensors. Combining multiple sensors on one platform presents even greater challenges to available bandwidth.
Applying Computing Power
Imaging systems can address the bandwidth challenge by using computing technology to make better use of existing data-link bandwidth. Processing power co-located on a sensor platform can be used first to turn raw data into images, then for image compression, and, at the most sophisticated level, to execute image exploitation algorithms such as change detection in comparing two images. Each level requires more computing power but enables the data link to be used more efficiently to transmit useful information.
On large platforms–ships, wide-body aircraft, or ground vehicles–sophisticated imaging systems currently use onboard integrated computing engines that process the sensor data. In most cases the users, or at least the first echelon of users, are the crews staffing these large platforms. The users apply human intelligence to the timely analysis of the sensor-based images, supporting the effectiveness of large platforms.
In contrast, unmanned aerial vehicles (UAVs) offer a different set of significant advantages as platforms for imaging systems. UAVs can sustain very long surveillance missions without crew exhaustion. They can be constructed without any systems to support a crew and do not have to be built to fit human dimensions. And, most importantly, no lives are sacrificed if they are lost to accident or enemy action.
Early UAV implementations, such as the Global Hawk and Predator UAVs (Figure 1a), are fairly large platforms. Their size makes them capable of supporting the same types of sophisticated imaging systems as manned platforms, supported by the same types of integrated computers, transforming raw data to images. The images must, of course, be transmitted to ground stations for human analysis.
Small UAV Platforms
A parallel trend is to use increasingly smaller UAVs (Figure 1b). Defense forces always need more intelligence-gathering assets, and smaller UAVs can be deployed in great numbers, often attached directly to tactical units serving in the field. The operational trade-off is that the sensors supported by these smaller UAVs are currently limited to video cameras, which are compact, lightweight, and do not require computer processing to generate images that can be carried on available communications bandwidth.
Deploying large numbers of small UAVs generating video images is a positive step for military operations. The next step is to merge the two trends by shrinking the sophisticated sensor systems so they can be deployed on smaller UAV platforms.
One option is to just shrink the sensors and dispense with onboard computers, simply relaying the raw sensor data to ground stations for processing. However, this approach raises the communications-bandwidth challenge; data links are not able to handle the raw data stream from sophisticated sensors such as a SAR. To be truly effective, small sensor-based systems must be supported by a new generation of signal processing computers–powerful, rugged and ultra-compact.
Typical System Requirements
While needs vary across a range of Small UAV implementations, requirements for this next generation of ultra-compact computing can be summarized as follows:
Greater than 100 GFLOPS: Systems can be implemented with less than 100 GFLOPS of processing power, but image-exploitation algorithms, such as change detection, geo-registration, or automatic target recognition demand that level of processing or more.
Less than 10 pounds: There is a widely deployed tactical category of smaller UAVs with a total payload capacity ranging from 60 to 200 lbs. In a general sense, it is reasonable to allocate up to 10 lbs. of that payload capacity to computing, but not much more.
Significantly smaller than half-ATR form factor: The ATR system for standardized electronics packaging evolved to meet the needs of deployment in manned aircraft. New generations of UAVs are not built to fit human dimensions, so it is not surprising that the ATR form factor, even in its half-ATR short form, is simply too big.
A range of I/O protocols: An embedded computing system that is processing sensor input must be flexible enough to support multiple types of sensors. Sensor payloads can change from one type to another, or use multiple types within one payload.
Flexible, networked configurations of sensor systems: To gain maximum effectiveness from multiple UAV deployments, computing solutions must be network-centric, capable of moving imagery information in a dynamic fashion using minimal bandwidth from one intelligent node to another. For example, overlaying images from different sensors can happen a lot more quickly, and easily, when all the sensors and the image processing computers are connected on a network.
Rugged enough to withstand difficult environmental conditions: Defense electronics systems must perform in harsh environmental conditions, including excessive heat, humidity, poor air quality, high altitude, shock and vibration. These embedded computers must not overheat, even when temperatures range up to 55°C and the air is too thin to be used for cooling. At the same time, they must possess the mechanical integrity to withstand high shock and vibration forces.
A general trend that’s aided the drive for reducing the size and weight of UAV-based embedded computing systems is a move toward stand-alone rugged boxes. Embedded board vendors are adding stand-alone rugged box-level systems to their military market offerings. These complete system boxes–which often support standard form factor boards inside them–provide a complete, tested and enclosed computing solution that eliminates complex integration chores for customers. Currently there’s about a dozen or more vendors that have some sort of stand-alone rugged box-level system in their offerings–many even have whole product lines in that category.
An example along those lines is the PowerBlock 50 from Mercury Computer Systems. This fully integrated ultra-compact embedded computer has 6 slots for processing or SATA storage modules, interconnected by a high-bandwidth PCI Express switch fabric. Processing cards include P.A. Semi PA6T-1682M, PowerQUICC III and Intel processors, and Xilinx Virtex-4 FPGAs, with a maximum processing performance of up to 172 GFLOPS per system.
This unit is enclosed into a package measuring only 4.1 x 5.3 x 5.8 inches (105 x 134 x 148 mm) and weighing less than 7 pounds (Figure 2). The chassis is designed throughout to isolate its internal electronics from all external environmental and physical conditions, enabling deployments in harsh environments. Rugged features include o-ring sealing for pressure, humidity and EMI isolation, high-reliability connectors, extended temperature ranges, and locking modules for shock and vibration immunity Each processing card can be mated with a dedicated I/O daughtercard into a single module, enabling, for example, a direct interface to an FPGA for real-time I/O processing.
Mercury Computer Systems