Data Fusion

Data Fusion is the process of combining multiple data in order to produce information of tactical value to the user. Data can come from one or many sources. Sources may be similar, such as radars, or dissimilar, such as electro-optic, acoustic, or passive electronic emissions measurement. A key issue is the ability to deal with conflicting data, producing interim results that the algorithm can revise as more data becomes available. Daniel H. Wagner Associates was one of the earliest developers of Kalman Filter Trackers and Multi-Hypothesis correlators. We are still at the forefront of fusion technology by combining Non-Gaussian (or "probability map") target modeling with standard Kalman Filters in the same algorithm. This is especially useful for tracking targets with low probabilities of detection and complicated motion. This page contains some of our notable achievements in Data Fusion.

Detailed Autonomous Unmanned Surface Vehicle (AUSV) Modeling and Simulation System (DAMS)

Teamed with NASA’s Jet Propulsion Laboratory (JPL) and Spatial Integrated Systems (SIS), we developed open system architecture (OSA) modeling and simulation (M&S) components that:

  • Simulate an entire environment using an intuitive user interface
  • Execute a complex scenario with

CNO Swarm Demo

Wagner’s Data Fusion Engine (DFEN) was used to generate a Common Operational Picture (COP) across five Autonomous Surface Vehicles (ASVs), based on sensor data from each ASV, during the CNO Offensive Swarm demonstration in August 2014 on the James River.  DFEN also associates and fuses acoustic

Decentralized and Autonomous Data Fusion Service (DADFS) for Heterogeneous Unmanned Vehicles (UVs)

DADFS “burns down the haystack” by significantly reducing clutter

  • Alerts operators concerning high-interest tracks
  • Prioritizes targets for operator investigation
  • Improves track persistence
  • Improves object classification and ID estimates (accuracy and latency)
  • Minimizes

Feature Aided Association Module (FAAM)

Developed for the U.S. Army Space and Missile Defense Command (USASMDC), FAAM is a atand-Alone Software module for utilizing sensor kinematic and non-kinematic feature (i.e. attribute) data measurements to more accurately calculate a probabilistic ID estimate and measurement-to-track association

Relevant Common Tactical Picture (RCTP)

RCTP creates a decluttered, accurate, and near-zero latency Common Tactical Picture (CTP) or Common Operational Picture (COP) on each Command and Control (C2) node based on all available kinematic (i.e., positional) and non-kinematic (i.e., attribute) data from all nodes and theater and national

Improved End-Fire Tracking Algorithms (IETA)

IETA resolves torpedo and other acoustic targets in end-fire and automatically generates an accurate and operationally relevant passive acoustic Situational Awareness (SA) picture (including target classification estimates).  IETA has successfully processed both simulated and real-world passive

Net-Centric Data Fusion (NCDF) for Undersea Warfare Decision Support System (USW-DSS) and SSQ-89A(V)15 Torpedo Defense Functional Segment (TDFS)

NCDF is a low-cost system solution (straightforward integration of mature, extensively tested components) providing:

  • An accurate and fused Common Tactical Picture (CTP), utilizing all available networked sensor data
  • An automated multi-sensor system with a high probability of torpedo attack detection (PD) and a low false alarm rate (FAR)

NCDF, CTAM and TOAM components have been integrated into Undersea Warfare Decision Support System (USW-DSS) Build 2 in order to provide automatically generated cross-platform track

Combat Air Identification Fusion Algorithm (CAIFA)/Composite Combat Identification (CCID) Reasoning Algorithm

Wagner was awarded a follow on BAA contract sponsored by the Office of Naval Research (ONR) to enhance CAIFA, a Bayesian Network-based reasoning algorithm used to create and maintain an accurate air picture by providing a common algorithm for theater-wide identification.

CAIFA creates and maintains

Object Avoidance for Unmanned Surface Vehicles (OAUSV) for NSWCCD and ONR Autonomous Maritime Navigation (AMN)

In this project for NSWC-DD/NAVSEA Wagner developed a system that processes all available data, dynamically generates a Tactical Picture, an optimal route, and an object avoidance plan, and provides this information to the Unmanned Surface Vehicle (USV) control system and its operators. A key capability

Ground Target Tracking and Identification System (GTIS)

Fuses all available data using Bayesian inferential reasoning, multiple hypothesis association, and non-Gaussian tracking techniques.  Able to process data from large numbers of diverse Ground Moving Target Indicator (GMTI), Signals Intelligence (SIGINT), Imagery Intelligence (IMINT), Measurement