TECHNOLOGY AREA(S): Air Platform, Battlespace, Electronics

ACQUISITION PROGRAM: PMA262 Persistent Maritime Unmanned Aircraft Systems

The 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.

OBJECTIVE: 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.

DESCRIPTION: 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.

Achieving this capability depends on both the sensitivity of the passive receiver to discern the signature information content and the development of automatic processing algorithms that is able to robustly differentiate and classify the information. For this SBIR topic it will be necessary to determine the passive receiver requirements and to develop the processing techniques. Approaches are desired that can be integrated into fielded systems with minimal modifications are desired.

PHASE 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.

PHASE II: Demonstrate technical capability with real data using a radio frequency detector. Quantify effectiveness and performance.

PHASE 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 under surveillance.

REFERENCES:

1. Lund�n, J. and Koivunen, V. �Automatic radar waveform recognition.� IEEE Journal of Selected Topics in Signal Processing, June 2007. Vol. 1, No. 1, pp. 124�136. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.131.6784&rep=rep1&type=pdf

2. Zhang, M., Diao, M., Gao, L., and Liu, L. �Neural Networks for Radar Waveform Recognition.� Symmetry 2017, 9(5), 75. doi:10.3390/sym9050075

3. 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), DOI 10.1109/ICASSP.2017.7952594

KEYWORDS: Automatic Radar Waveform Detector; Passive Radio Frequency Sensors; Low Probability of Intercept; Radar; Waveform Recognition; Emitter Locating