In two SBIR programs for USAF/ESC Hanscom AFB, MA,
Daniel H. Wagner Associates showed how our track-to-track algorithm could
efficiently combine realistic E-3 sensor data, including ESM, pulse-Doppler radar,
IFF and bistatic sensors to produce a correlated picture. We also used MATCH
to perform the contact-to-track association at the sensor level
for those sensors which do not provide unique track IDs. We implemented
a comprehensive error detection and management scheme for handling
sensor anomalies such as radar ghosts, false alarms, IFF garbling,
etc., to produce a correlated picture.
We developed prototype software in Ada to build scenarios, generate
simulated sensor data, and process the data using our algorithms.
Six scenarios were proposed by the Air Force and generated and
processed using our in-house resources.
In addition, we developed several Measures of Effectiveness based on
our research on track-to-track correlation, and two of these were used to characterize
the performance of our prototype software.
The software, referred to as the Multiple Sensor Statistical
Likelihood Estimator (MUSSLE), has been successfully installed
in the USAF Fusion Evaluation Testbed and underwent an exhaustive
final testing and evaluation process, with outstanding results.
As part of this work, Wagner Associates developed
concepts for improved ID Fusion. We implemented the new ID functions,
determined the
operator interface requirements, and also enhanced the efficiency
of the software to handle large volumes of real-time data. We also added the
capability to process offboard track reports.
Wagner Associates has also performed related work for the Air Force Research
Laboratory on Level 2 Data Fusion
Algorithms.
