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Tracking > NRTDF
Near-Real-Time Data Fusion (NRTDF) and Testbed
NRTDF is a full-capability data fusion engine that utilizes both linear (Kalman
Filter) and non-linear (Monte Carlo) tracking models. The Kalman filter is
used for all tracks and is supplemented by the Monte Carlo model for
high-interest and/or low reporting rate targets. NRTDF is an advanced and
highly processor-efficient version of the Global
Correlation Engine (GCE) developed by Daniel H. Wagner Associates for Naval Surface
Warfare Center Dahlgren Division.
Contents
Fusion Testbed
The Fusion Testbed has been created to test and demonstrate the
NRTDF for different scenarios. The testbed has an easy-to-use GUI and database
(yellow)that allows an analyst quickly to create, modify, store, and run
multi-target scenarios to supply reports to the correlator. The
SRAPS module can simulate one or more reactive undersea targets. The data
generator has high-fidelity sensor models for radar, ESM, acoustic and other
sensors, along with a special module to evaluate the performance of the
correlator.
The testbed has been used to evaluate typical surveillance scenarios for
the Navy's new SH60R common helicopter. NRTDF will form the core of its
cockpit situation awareness and mission management software.
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The Scenario GUI and Database are an integrated package. The
Data Generator runs on output data files created by the GUI. The Data
Generator and the SRAPS Simulation separately send reports to the Input
Process. The M-H Association process shares data on high-interest tracks
with the Non-Gaussian module. The Optimizer uses the Non-Gaussian Output
to develop search patterns. |
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Testbed Graphical User Interface
A sample of the Testbed Graphical User Interface is shown below. The user
can point and click to create simulated targets, place them in the scenario,
and specify movement and detectability. The user can also define sensor
capabilities for friendly platforms, and turn on or off sensors during the
scenario. The user can mix simulated reports with real reports during replay
for post-scenario analyses.
Multi-Hypothesis Correlation
The core of the NRTDF is a Multi-Hypothesis correlator, called
MATCH that can handle multiple sensor types, multiple
platforms, out-of-sequence reports, and both kinematic and attribute-based
sensors. The multiple hypothesis method is important because it allows MATCH
to correct improper associations quickly. When an association is ambiguous,
MATCH creates multiple models and holds the most likely ones in memory. The
most likely hypothesis is displayed to the operator but, when new data
indicate a change, the correct hypothesis is quickly substituted.
Example Application to
Passive Tracking
The example shown below, created in the testbed, demonstrates this
capability. There are two friendly ships with ESM capability both reporting to
a common correlator. There are two hostile ships with emitting radars, beyond
radar range of either friendly. Both friendlies gain passive contact on both
enemies.
NRTDF can handle large numbers of tracks at very high data rates because of
its unique "data bypass" feature, where reports are prioritized and can bypass
MATCH when the data rate exceeds the processor capacity.
Non-Gaussian Tracking
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A unique feature of NRTDF is its ability to create high-fidelity
target models for high-interest targets that are undetected. These
models are based on the Monte Carlo method and can use INTEL information
plus negative information provided by unsuccessful search. As opposed to
standard trackers, the Monte Carlo model can handle the case where the
target can take alternative, distinct trajectories.
A "probability map" shows a snapshot of the location distribution of
a high-interest target.
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