Artificial intelligence (AI) and machine learning in sensor, signal, and image processing

Hosking at Mercury Systems says that in addition to target identification with EO/IR, SAR, and radar imagery, “Cognitive radar systems dynamically adjust waveforms based on environmental conditions and threats. AI improves clutter suppression, reducing false alarms in maritime and airborne surveillance. machine learning-based electronic warfare (EW) threat classification enables real-time signal identification and jamming. Bayesian networks and deep learning improve sensor fusion for more accurate tracking of fast-moving threats, and AI-driven data association algorithms resolve conflicting sensor inputs and enhance object correlation.”

Mercury’s DRF2270 System-on-Module (SoM) and DRF5270 3U board are the latest additions to its direct RF digital signal processing product line, using Altera FPGAs to analyze data across a broad range of the electromagnetic spectrum. The DRF2270 is an eight-channel SoM capable of converting analog and digital signals at a rate of 64 gigasamples per second.

The DRF5270 integrates the DRF2270 SoM into a 3U defense-ready board, featuring 10, 40, and 100 Gigabit Ethernet optical interfaces. The modular design of the SoM allows for customization to specific applications without requiring a full board redesign. Additionally, the DRF2270 can be incorporated into other small-form-factor or custom configurations.

Speed needs

Bob Vampola, vice president of aerospace and defense business at Microchip Technology in Chandler, Ariz., identified a quartet of trends driving the development needs of today’s signal processing technologies. First, Vampola says there is a need for more resolution at a higher number of frames per second.

“One consequence is higher bus speeds to accommodate the increase in data,” Microchip’s Vampola says. “The second is high-speed Ethernet for the data bus. This trend isn’t new, however, it is gaining speed and broader acceptance. The third, for many imaging applications, constraints like available power, power dissipation and available volume drive a non-GPU approach. The trade-off is around performance (typically speed) vs size and power budgets. And lastly, image processing at the edge is gaining traction. In this case, edge means placing a dedicated processor near the camera, extracting meaning from the image data there, and transmitting the meaning rather than the entire image data to a more central node. The result is lower bus speeds and relaxed thermal management concerns.

“As compute workloads move to the edge, [Microchip’s] PolarFire SoC and PolarFire FPGAs offer 30–50% lower total power than competing mid-range FPGAs, with five to ten times lower static power, making them ideal for a new range of compute-intensive edge devices, including those deployed in thermally and power-constrained environments,” Vampola continues. “PolarFire SoC and PolarFire FPGA Smart Embedded Vision solutions include video, imaging and machine learning IP and tools for accelerating designs that require high performance in low-power, small form factors across the industrial, medical, broadcast, automotive, aerospace and defense markets.”

Curtiss-Wrights’ Smetana also identified four sensor processing trends in mil-aero systems, including the necessity to manage a large volume of high-resolution data, resulting in a demand for increased bandwidth capacity. “In order to accommodate the higher bandwidth there is a stronger push for 100 Gigabit Ethernet fabrics, Gen4 or even Gen5 PCIe links, and an acceleration in smart sensors with fiber optic connections to FPGAs,” says Smetana, acknowledging a growing demand for several intelligence (multi-INT) capabilities and sensor fusion and the necessity of incorporating GPUs to assist with the required processing volume.

“Although this has to be balanced with the power and heat they generate and challenges related to securing GPU data,” notes Smetana. “For larger systems where 6U VPX cards are used, we are seeing a significant increase in the need for Liquid Flow Through (LFT) cooling as it is the only way to cool the higher-end processing devices whether they are general purpose processors, FPGAs, or GPUs.”