SensorFusionAI

Sensor-Agnostic, 3D Data Fusion Engine for Complex Environments

Detection of drones or Unmanned Aerial Systems (UAS) is moving towards a multi-sensor approach for fixed site (and in certain situations, vehicles and ship systems), where the space and budget allows (due to ability to provide better detection results with multiple sensor modalities, such as Radio Frequency, radar, acoustic and camera systems). This can be deployed in a single or across multiple nodes. 

However the multi-sensor approach only generates better results, with an intelligent software engine to fuse together the sensor outputs and give an intelligent set of outputs – otherwise adding more sensors is counterproductive as it creates more data without a clear way to manage it.

DroneShield has developed a true AI-based SensorFusion engine, SensorFusionAI (SFAI), initially for its own DroneSentry-C2 Command-and-Control system, including all common drone detection modalities (RF, radar, acoustics, camera), is now available for third party C2 manufacturers to add SFAI to their C2 systems, thus improving the performance of their C2 offerings.

Features

  • Behaviour Analysis: track an object to determine classification and predict trajectory

  • Threat Assessment: intelligently determine threat level based on a wide range of data types

  • Confidence Levels: designed for complex, high noise environments, with inconsistent data inputs

  • After-Action Reporting: sophisticated analytics presented in easy to interpret graphical dashboards

  • Edge Processing: utilises an edge processing device (SmartHub) for reduced network load and high scalability

  • Versatile Adaptable Inputs: new sensors use existing software adaptors to improve integration time

  • Output to Any Platform: visualisation on DroneSentry-C2 or third-party C2 platforms, data analysis, alert systems or security management software

Direct Correlation

Direct Correlation: Utilises methods such as temporal and spatial correlation for object confirmation.

  • Direct Correlation: Utilises methods such as temporal and spatial correlation for object confirmation.

  • System attempts to correlate raw sensor data in isolated incidents, data is not weighted based on the environment

  • Hard sensitivity selection will either generate high numbers of false positives or false negatives

  • Results in inconsistent paths or dropped detections

  • Conflicting or bad sensor data can present inaccurate results, system trust is broken

  • Cluttered display and need for human analysis increases operator cognitive load

Data Flow

1 - Observe:

Ingests data from all sensor modalities and manufacturers. Available APIs for multi-sensor technology data output ingestion. Random Finite Sets analysis to determine emitter presence (Object Existence Estimation) and tracks (Object Count Estimation).

2 - Orient:

Predicts track of detected objects that have disappeared from view. Single-object tracking via Non-linear Multi Hypothesis State Estimation, Gaussian Sum Filters for measurements and Motion Models for Prediction.

3 - Decide:

Fuses multiple sensor “hits” if they are from the same UAS. Multiple Object Distinction and Tracking via the use of Density Clustering models and Joint Probabilistic Data Association.

4 - Act:

Generates UAS tracks rather than points. Actionable intelligence with rich information, threat level and confidence level. Track Oriented - Multi Hypothesis Tracker (TO-MHT) for data association and multiple-object tracking.

Correlation vs SensorFusion AI

SensorFusionAI

SensorFusion: Sensors are utilised for their strengths with their weaknesses offset by the strengths of sensor types.

  • System intelligently builds a model informed by all inputs over time

  • Confidence values allow for soft sensitivity selection, reducing false positives or false negatives

  • Prediction model can interpolate paths for consistent tracking even with sparse data

  • Any incomplete or contradictory data mediated by comprehensive object model

  • All sensor data fused into one consistent intelligence packet

Our Approach

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