Department of Defense

1) Combat Identification (CID)

a. Navy: Target Identification Interrogation Data Stream Analytics System

AEGIS air defense teams spend an inordinate amount of time trying to identify aircraft and distinguish hostile intent. Applying machine learning to large volumes of surveillance and signals intelligence data significantly decreases operator workload on subordinate tasks, freeing cognitive capacity to be more effectively applied toward tactical warfighting decisions. 

Mosaic department of defense AI solutions improve the probability of aircraft identification and reduce the need for verbal communications. They take advantage of extremely large volumes of track data and create long-term memory of aircraft characteristics, defining the expected or “normal” characteristics for a specific airframe. The improved combat system track identification methodology enables the entire carrier strike group air defense team to focus more time on how they will employ which weapon instead of trying to sort out who is flying, where, and why. This combat efficiency is critical in a highly challenging environment with a peer competitor and weapons constrained load-out.

b. Improved and More Robust Automatic Target Classifiers

Mosaic is researching cutting-edge machine learning technology for Naval Aviation to develop tools capable of identifying an air contact of interest even when very little historical data is available. In classical deep learning classification problems, hundreds or thousands of training examples are needed per category to obtain a high degree of accuracy without overfitting the model. However, in many cases, this data is just not available. 

To overcome this challenge, Mosaic is building classification algorithms that merge machine learning models with traditional first-principle, physics-based models to provide an algorithm that can achieve high accuracy in spite of relatively low amounts of data.  These tools significantly increase aircrew cognitive capacity to make high-level tactical decisions, reduce the need for verbal communications, and effectively respond to inbound threats with the right weapon at the right time.

3) Computer Vision (CV), Imagery Annotation, and Imagery Segmentation

a. Army: Automated Imagery Annotation and Imagery Segmentation for Military Tactical Objects

Data annotation and segmentation at scale is one of the biggest bottlenecks facing the defense and intelligence community. Improving the flow of data is necessary to enable next-generation tools, models, and targeting systems. Given the domain-specific nature of artificial intelligence-based solutions, existing commercial tools cannot meet the needs of the US defense community’s unique problems. Mosaic provides an adaptable, modular, and automated labeling solution that evolves with the rapidly changing environments faced by modern warfighters. We are working on a solution that reduces the burden to label tactically relevant information in full-motion video and still imagery, enabling automated high-quality data preparation at scale for the warfighter. We recognize that improving data pipeline efficiencies from point-of-collection to pre-and post-processing and into the hands of scientists, engineers, analysts, and decision-makers is critical to information warfare engagement.

b. Navy: Multi-Source Imagery and Geo-positional Exploitation

Mosaic ATM is developing a software-based capability for automated sensor metadata correction in maritime and littoral environments for deployment into the Intelligence Carry-On Program (ICOP).  These environments present confounding challenges to traditional image-based geo-registration tools because reference imagery cannot be defined for regions of blue and green water.  

Mosaic’s budding solution addresses this problem by jointly processing full-motion video with geo-positional feature information obtained from correlated intelligence sources such as Automatic Identification System messages from maritime vessels, radar tracks from maritime surveillance radar systems, and signals intelligence cues to define dynamic maritime landmarks with which to pseudo-register the corresponding imagery. Significant corrections to errors in sensor metadata become possible and vastly improve the pixel geo-localization accuracy, as well as geo-positional feature overlay accuracy and intelligibility.