Blending Classical Model-Based Target Classification and Identification Approaches with Data-Driven Artificial Intelligence
Navy STTR 2018.B - Topic N18B-T033
NAVAIR - Ms. Donna Attick - firstname.lastname@example.org
Opens: May 22, 2018 - Closes: June 20, 2018 (8:00 PM ET)
TECHNOLOGY AREA(S): Air
Platform, Information Systems
ACQUISITION PROGRAM: PMA-262
Persistent Maritime Unmanned Aircraft Systems
OBJECTIVE: Develop innovative
techniques that combine the robustness and explainability of model-based target
identification and classification approaches with the potential to optimally
establish feature vector coefficients using data-driven, deep-learning
DESCRIPTION: The most robust
state-of-the-art target classification and identification approaches rely on
expert knowledge and model driven principles to mimic the methods used by
expert human operators in manual target classification and identification.
These approaches rely on the identification of a set of target features that
allow one target to be confidently separated from other, different targets.
This STTR topic seeks to evolve from classification ranking systems based on a
distance metric with pre-assigned feature weights to a more optimal classifier
in which the selection of features and their weights are determined
automatically from a statistical analysis of the collected feature vectors data
for each sensor. Such an approach provides the rationale for feature selection
and weighting as it is based on measured feature variance vector for each
sensor. Furthermore, the statistical analysis of collected feature vectors
enables the calculation of accurate matching confidence within the classifier.
This provides the needed explainability to quantify to what extent the operator
should trust the classification results. The efficacy of the approach should
improve with the accumulation of operational data. Sensor systems to be
considered are imaging radar, electro-optics, imaging infrared, and electronic
support measures. Applications include maritime vessel and overland vehicle
classification and identification.
PHASE I: Assess feasibility
of incorporating deep-learning approaches into target classification and
identification beginning with a statistical analysis to determine the relative
importance of features derived from templates and progressing toward the
determination of feature vectors from sensor data. Outline methods and
determine feasibility of evolving from a traditional approach of matching
measurements to pre-defined features and weights, incorporating data-driven,
deep-learning approaches to determine feature vectors from field data. This
would include the association of field feature measurements for each sensor
with database templates, registration and statistical analysis of all sensor
feature measurements, determination of relative importance of features derived
from templates, and deep-learning methods for determination of feature vectors
from field data. Finally, develop an architectural plan for in-flight,
operator-assisted database augmentation. Develop prototype plans to be
developed under Phase II.
PHASE II: Further mature
Phase I-developed algorithms and architectures for integration into the target
classification and identification application. Plan requirements for and
conduct data collection to support algorithms and software testing.
PHASE III DUAL USE
APPLICATIONS: Perform any required modifications to the algorithm and real-time
code to be hosted in the transition Program of Record as desired by the Navy.
Support modeling and simulation efforts as well as software integration, field
testing, and performance analysis in the specific application. The application
will need to adapt to the sensor capabilities and interfaces on the specific
aircraft type. Ultimately, the goal is to correctly classify a ship to find
naval class from amongst all of the combatants and non-combatants of the world.
Non-naval vessels can be classified to general type. Maritime activities such
as in the U.S. Coast Guard, shipping monitoring, and the Department of Homeland
Security—any officials that have the need to know what ship traffic exists—can
benefit from this technology. The basic core of the algorithms and fusion may apply
to land-based commercial vehicle tracking as well.
1. Geng, J. et al. “Deep
supervised and contractive neural network for SAR image classification”. IEEE
Transactions on Geoscience and Remote Sensing, Volume 55, Issue 4, April 2017. http://ieeexplore.ieee.org/document/7827114/
2. Mason, E., Yonel, B., and
Yazici, B. “Deep learning for radar”. Radar Conference (RadarConf) 2017 IEEE,
pp. 1703-1708, 2017, ISSN 2375-5318, http://ieeexplore.ieee.org/document/7944481/
3. Nguyen, A. et al., “Deep
neural networks are easily fooled: High confidence predictions for
unrecognizable images”. Computer Vision and Pattern Recognition (CVPR), IEEE,
4. Zhang, L., Zhang, L., and
Du, B. “Deep learning for remote sensing data: a technical tutorial on the
state of the art”. Geoscience and Remote Sensing Magazine IEEE, Vol. 4, pp.
22-40, 2016, ISSN 2168-6831. http://ieeexplore.ieee.org/document/7486259/
Intelligence; Deep Learning; Data Driven; Expert System; Maritime