Defense OS is the software platform developed by Black Sage to orchestrate and intelligently allocate defense assets. It’s interoperable and supports fusion of active and passive sensor data, target classification, precise pointing of sensors and effectors, threat prioritization, geofencing, track deconfliction, scaling of assets across large and disparate geographies, and much more.
Defense OS uses neural nets which are software appliances designed to recognize patterns in a way similar to the human brain. Neural nets are trained to recognize patterns in new target data and quickly determine the probability of a target belonging to one class or another.
ClassifICATION Performance Validation
Cross validation is the method for evaluating performance of the target classifier. When classification data is added to the system, a new classifier is generated and a five-fold 80/20 random sampling cross-validation occurs. Each validation step returns true and false positive rates and generates a receiver operating characteristic curve indicating objective classification performance. The performance from each of these steps is averaged and displayed to the operator in the DefenseOS dashboard.
Flexible Attribute Selection
Many target attributes are available from modern sensors including those that describe objects: width, height, latitude, longitude, velocity, acceleration, heading, surface area, displacement, and more. These make up a multidimensional signature of an object and are used by the classifier for pattern recognition. With the power of many available target attributes, operators can solve seemingly intractable false alarm problems by choosing exactly which are included to regulate classifier performance and behavior.
Bird Vs UAS
Distinguishing between birds and UAS is a common challenge when monitoring airspace. AI recognition of size, heading, lat, lon, velocity and acceleration patterns makes this task reliable and automatic.
Person Vs Vehicle
People and vehicles often look the same to deterministic alarms. AI is able to tease out unique characteristics like the paths and times of travel in order to classify accurately when deterministic alarms create false positives.
Rotorcraft Vs Fixed Wing
Rotorcraft and fixed wing aircraft share many similar flight characteristics, making them difficult for deterministic systems to distinguish. AI does this readily by learning from altitude deltas and surface area.
The classifier is agnostic to sensors, attributes and classes. Like the human brain, it can be trained on nearly any type of input data and will learn and change behavior accordingly.
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