Automatic Threat Radar Waveform Recognition
Navy SBIR 2019.1 - Topic N191-011
NAVAIR - Ms. Donna Attick - firstname.lastname@example.org
Opens: January 8, 2019 - Closes: February 6, 2019 (8:00 PM ET)
AREA(S): Air Platform, Battlespace, Electronics
PROGRAM: PMA262 Persistent Maritime Unmanned Aircraft Systems
technology within this topic is restricted under the International Traffic in
Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and
import of defense-related material and services, including export of sensitive
technical data, or the Export Administration Regulation (EAR), 15 CFR Parts
730-774, which controls dual use items. Offerors must disclose any proposed use
of foreign nationals (FNs), their country(ies) of origin, the type of visa or
work permit possessed, and the statement of work (SOW) tasks intended for
accomplishment by the FN(s) in accordance with section 3.5 of the Announcement.
Offerors are advised foreign nationals proposed to perform on this topic may be
restricted due to the technical data under US Export Control Laws.
Develop an automatic radar waveform detector using passive radio frequency
sensors such as existing radar receivers to detect, discern, classify, locate,
and track low-probability of intercept (LPI) radars.
The Navy is seeking algorithms and processing technology that can automatically
determine radar waveform parameters to detect, discern, and classify LPI
radars. Waveform parameters include for example: bandwidth, waveform
flexibility, phase shift coding, pulse code modulation as well as signal
strength and direction. Time-frequency analysis and machine learning techniques
have shown the potential to achieve automatic radar waveform recognition.
Recent open literature has begun to address LPI waveform recognition techniques
utilizing feature extraction and classification techniques to extract features
from the intercepted signal and to classify the intercepted signal based on the
extracted features. We seek to refine and extend such techniques.
I: Develop techniques and demonstrate the potential to derive automatic radar
waveform profiles with passive radar sensing using simulations. Determine
potential performance for different passive radio frequency receiver sensors.
Evaluate the potential performance to detect LPI radars and determine location
information to aid tracking. The Phase I effort will include prototype plans to
be developed under Phase II.
II: Demonstrate technical capability with real data using a radio frequency
detector. Quantify effectiveness and performance.
III DUAL USE APPLICATIONS: Complete development, integration, and transition to
Naval airborne surveillance platforms. The general approach may find use in law
enforcement applications where LPI communication techniques are used by those
Lundén, J. and Koivunen, V. “Automatic radar waveform recognition.” IEEE
Journal of Selected Topics in Signal Processing, June 2007. Vol. 1, No. 1, pp.
Zhang, M., Diao, M., Gao, L., and Liu, L. “Neural Networks for Radar Waveform
Recognition.” Symmetry 2017, 9(5), 75. doi:10.3390/sym9050075
Wang, C., Wang, J., and Zhang, X. “Automatic radar waveform recognition based
on time-frequency analysis and convolutional neural network.” 2017 IEEE
International Conference on Acoustics, Speech and Signal Processing (ICASSP),
Automatic Radar Waveform Detector; Passive Radio Frequency Sensors; Low
Probability of Intercept; Radar; Waveform Recognition; Emitter Locating