OmniSIG™ Sensor

The OmniSig sensor provides a new class of RF sensing and awareness using DeepSig’s pioneering application of Artificial Intelligence (AI) to radio systems. Going beyond the capabilities of existing spectrum monitoring solutions, OmniSIG is able to not only detect and classify signals but understand the spectrum environment to inform contextual analysis and decision making. Compared to traditional approaches, OmniSIG provides higher sensitivity and accuracy, is more robust to harsh impairments and dynamic spectrum environments, and requires less computational resources and dynamic range. The OmniSIG software can be deployed on a wide range of target devices ranging from low-SWaP mobile and embedded systems to cloud-based high-performance computational clusters. It provides a web-based UI as well as an open low-latency streaming interface and control API for seamless integration into customer systems and applications.


The OmniSIG sensor is able to perform detection and classification of RF emissions across very large bandwidths of spectrum on the order of milliseconds, giving it the ability to report anomalies, changes, or threats in near real-time. It works with both wide- and narrow-band signals, and delivers accurate results for highly dynamic signals and within harsh or contested environments. Detection and recognition has been validated across a wide range of signal types, including numerous QAM, PAM, FSK, analog single carrier modulations, multi-carrier modulation schemes, cellular and infrastructure signals (e.g., GSM, LTE, WiMAX), ISM-band signals (e.g., WiFi, BlueTooth), and mobile radio services (e.g., P25, GMR, PTT), and can be readily extended to include additional signals and protocols based on customer requirements and applications. It is robust to interference, both intentional and unintentional, and to other impairments like those caused by receiver hardware.

Deploying with OmniSIG

The OmniSIG sensor is a software product that customers can integrate into their own systems or third-party platforms. The OmniSig software is highly flexible, and can be targeted to a wide variety of processing platforms and elements, can use standard or custom radio interfaces, and is easily deployed and scaled using software including Docker containers.

The OmniSIG software typically requires the presence of at least one general purpose processor, such as an x86 or ARM core, and can accelerate signal processing throughput and efficiency using Graphics Processing Units (GPUs) including integrated NVIDIA Tegra cores or discrete GPU cards, Tensor and Vector processing accelerators, and FPGA resources. The OmniSIG sensor typically operates in a streaming fashion, ingests radio samples from many common radio interfaces, and can make use of packet formats like VITA-49. The web-based user interface can be used from any device with a browser, including mobile handsets, and the OmniSIG software also provides its metadata output stream in JSON form for use by other applications.

Theory of Operation


DeepSig’s revolutionary approach to signal processing applies machine learning directly to time-series radio samples and channel measurements. By creating algorithms that learn from raw signal data and effects, DeepSig’s systems achieve significantly better performance than traditional heuristic and expert-feature based techniques.

Our approach allows us to optimize the system as a whole for a particular set of performance requirements rather than optimizing individual components, as is the practice in existing methodologies.


The sensing techniques used within the OmniSig sensor have demonstrated an improvement of 4 to 10 dB more sensitivity than existing state-of-the-art methods, and can in some instances provide 10x or higher reductions in computational complexity through reduced sample precision and dynamic range requirements, enabling reductions in antenna size and receiver expense. It can also produce accurate sensing results with substantially reduced data requirements and dwell times, providing further power reductions and throughput increases on equivalent processors. Sensing can be easily parallelized, scaled to application needs, and deployed on everything from a cloud compute cluster to a mobile ARM processor.


To learn more about using OmniSIG in your applications, please contact us!

Name *