Modern C5ISR [command, control, communications, computers, cyber, intelligence, surveillance, and reconnaissance] platforms are only as effective as their ability to process and analyze the data gathered from sensors.
As the defense landscape becomes increasingly contested with sensors such as those used for radar, electro-optical/infrared (EO/IR), and radio frequency (RF), system architects face growing bottlenecks. The data gathered must be processed at the edge, but legacy systems lack sufficient compute power to keep up with multi-sensor processing demands.
Sensors form the digital backbone of nearly all real-time intelligence applications, driving critical decisions across ISR missions. But these inputs are no longer from a single source - next-generation command and control (NGC2) applications rely on multidomain sensor fusion to drive mission-critical decisions with greater speed and precision. By combining multiple sensing modalities into a unified operational picture, sensor fusion enables faster, more accurate threat detection and situational awareness in complex and contested environments. As ISR missions grow more data-intensive and time-sensitive, efficient sensor fusion is now a mission requirement.
In theory, adding more sensors should improve situational awareness. In practice, it creates massive complexity: Engineers are now tasked with correlating data from vastly different sources, each with unique resolutions, sampling rates, and coordinate systems, as well as synchronizing them in a coherent fashion.
The question becomes: What processing hardware can handle this data and process it effectively?
GPUs were built for parallel workloads. While CPUs excel at sequential logic and control flow, GPUs can simultaneously process thousands of high-resolution data streams, making them ideal for sensor fusion and other signal intelligence (SIGINT) applications.
A GPU's architecture is perfectly suited for the matrix-heavy computations found in DSP and artificial intelligence (AI) tasks like filtering, object detection, and multisensor correlation. With the flexibility to run both traditional signal processing and modern machine learning (ML) models, GPUs provide the performance and adaptability that ISR platforms need at the edge.
For airborne ISR platforms, GPUs enable real-time fusion of radar, EO/IR, and lidar sensor data by processing large data streams in parallel. They support both traditional DSP and AI-based target detection, so they are suited for missions where speed, precision, and low size, weight, and power (SWaP) are critical. In counter-uncrewed aerial system (C-UAS) and active protection systems, GPUs are essential for fusing high-speed sensor data and running AI models at the edge. Their ability to process visual, radar, and RF inputs simultaneously enables faster decision-making and reduces false alarms in complex, often cluttered environments.
From target recognition to threat prioritization, ISR systems are increasingly using AI. GPUs offer built-in support for AI toolchains like TensorRT, Holoscan, CUDA, and cuDNN, which eases integration of ML models directly into the fusion pipeline.
By processing multiple sensor streams in parallel, GPUs dramatically reduce the time it takes to ingest, correlate, and interpret data. They support complex DSP workloads and run AI models at the edge, enabling ISR platforms to classify targets, detect anomalies, and prioritize threats in real time.
The result? Shorter detection-to-decision loops, higher confidence in target identification, and a faster and more autonomous ISR cycle that delivers immediate tactical advantage.
Whether mounted on airborne ISR aircraft, ground vehicles, or autonomous platforms, GPU-accelerated systems support advanced capabilities like multitarget tracking, object classification, and dynamic threat prioritization, all without relying on backhaul links to centralized processing.
The extended capability at the edge to process this data also results in reduced latency, as data processing can move away from servers and command centers. This move is augmented even further with technologies such as GPUDirect RDMA and RDMA over Converged Ethernet (RoCE), which provide low latency, low-CPU memory transfers across sensor fabric.
Most of these technologies are easy to migrate from the lab to the field and are readily upgradeable and replaceable. Unlike FPGA [field-programmable gate array]-based solutions for AI, those developed for GPUs are cross-compatible with one another, even across new hardware generations, enabling easier upgrade pathways. GPU-based solutions also enable rapid prototyping and development of new sensor-based solutions when compared to ASICs [application-specific integrated circuits] or FPGAs.
As ISR platforms evolve to meet the demands of multidomain operations, sensor fusion has become the foundation of situational awareness and threat response. Fusing data from radar, EO/IR, RF, and other modalities in real time enables systems to deliver faster, more accurate intelligence directly at the edge, where seconds count. GPUs are a critical enabler of this capability, offering the parallel processing, low latency, and AI readiness required to turn massive sensor data into immediate action.
As defense programs increasingly adopt open software standards - including the U.S. Navy's USV Common Control System, which is aligned with the modular open systems approach (MOSA) and leverages Sensor Open Systems Architecture, or SOSA, guidelines - embedded GPU-based plug-in cards (PICs) are a flexible, future-proof way to approach real-time sensor fusion. Rugged 3U VPX and XMC GPU modules integrate seamlessly into these open systems, enabling faster upgrades, better interoperability, and reduced time to field.
As sensor fusion becomes the cornerstone of next-generation C5ISR applications, the need for deployable, high-performance compute power at the edge is more critical than ever. GPU-based solutions provide the ideal balance of raw processing power, AI/ML readiness, and system flexibility.