Aided Target Detection and Recognition

Leverage Machine Learning / Artificial Intelligence to aid decision making

AI-ENHANCED DETECTION

Leverage our expertise in signature analyses to optimally train signature-based object detection algorithms

As AI-based object detectors are becoming more integrated into the modern battlefield attention to performance, safety, and reliability must be considered. ML/AI technologies have proven to serve as a helpful “tool” to the Warfighter. However, it is not going to solve all challenges. As such, ML/AI applications must be developed in a manner that keeps pace with technological advancements but also provide the user with reliable and repeatable performance.

Signature Research’s approach toward ML/AI development seeks to couple sensing (including edge processing), best of breed object detector architecture, the “physics” behind multispectral signatures, and final use cases to optimize ML/AI Development.

What separates us from others:

Expansive and ever-growing dataset of measured and modeled threat imagery to aid in ML/AI training
Decades of multispectral signature measurement, modeling, and analyses supporting “real world” target signatures
Demonstrated ML/AI maturation, integration, test, and deployment
Complex understanding of sensing limitations (sensors, atmospherics, clutter, etc.) which impacts detector performance
Close collaboration with Academica to ensure SGR’s remains on the forefront of object detector architecture
Ability to support requirement development, system safety, and continual object detector maturation

IMPROVE TRAINING DATA AND ATR ROBUSTNESS

SAR ML MODELED ATR DATA AUGMENTATION PROCESS

SAR ML Modeled ART Data Augmentation Process Graphic

SIMULATING HUMAN PERFORMANCE

COMBINE OPERATOR INSIGHT WITH AI Efficiency

Integrating human-in-the-loop training enhances synthetic models, enabling them to emulate operator decision-making. This hybrid approach excels in scenarios such as assessing camouflage, concealment, and detection.

By iteratively refining AI performance, we also reduce the need for traditional field assessments. The result is a closed-loop system that accelerates evaluations, delivers reliable recommendations, and retains human oversight as a critical element in decision-making.

EXPLAINABLE AI (XAI)

Understanding the Why

Proving comprehensive insight into why a trained ML/AI algorithm performs as trained is a challenge facing industry today. To the greatest extent possible Signature Research seeks to provide our customers with valuable XAI (Explainable AI) information related to trained ML/AI detectors. This aids in tailoring the volume and complexity of training data required, provides insight into objector sensitives to specific signature characteristics, and provides insight into end item limitations of use. Signature Research XAI toolsets can rapidly interpret and assess a trained object detector’s performance against a given set of test imagery.

EXPLAINABLE AI

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