Radio Frequency (RF) waveforms.

 

DESCRIPTION: Create a set of Analog-to-Information (A2I) tools, suitable for use on embedded (FPGA Virtex7/Stratix10 class) and General Purpose Computer (GPC) systems (Intel Core/Xeon class), that have the capability to detect, process, generate, and classify non-periodic RF signals that do not exhibit sinusoidal characteristics such as UWB, Noise Radars, and LPD RF waveforms through the innovative use of advanced signal analysis techniques, which can include wavelet analysis, deep learning, multifractal analysis, cepstrum coefficients, Compressive Sensing (CS) and/or other feature extraction techniques. One of the goals of this effort is to leverage and adapt the current state-of-the-art developments from signal domains related to telecommunications, image processing, marine mammal monitoring, and structural health monitoring to enhance current technological development efforts related to modern spread spectrum and non-traditional signals encountered during military operations. This is as much a needed capability as the ability to detect and classify unknown signals is critical to operations in contested environments. It is expected that this effort would build upon and complement the previous work in other signal domains such as acoustic and image processing.

 

The proposed solution will be evaluated on the ability to detect, process, generate, and classify non-periodic RF signals that do not exhibit sinusoidal characteristics such as UWB, Noise Radars, and LPD RF waveforms. The specific waveforms will be a combination of both known waveforms to establish baseline performance and unknown waveforms that will be used to characterize performance.

 

It is anticipated that the hardware elements such as mixers, signal generators, signal analyzers, and Software Defined Radio kits required to develop, test and demonstrate performance already exist. Therefore, the proposed effort should focus on developing the algorithms, techniques and A2I tools and utilize Commercial Off-the-Shelf (COTS) equipment as much as practical.

 

Work produced in Phase II may become classified. Note: The prospective contractor(s) must be U.S. owned and operated with no foreign influence as defined by DoD 5220.22-M, National Industrial Security Program Operating Manual, unless acceptable mitigating procedures can and have been implemented and approved by the Defense Security Service (DSS). The selected contractor and/or subcontractor must be able to acquire and maintain a secret level facility and Personnel Security Clearances, in order to perform on advanced phases of this project as set forth by DSS and NAVAIR in order to gain access to classified information pertaining to the national defense of the United States and its allies; this will be an inherent requirement. The selected company will be required to safeguard classified material IAW DoD 5220.22-M during the advanced phases of this contract.

 

PHASE I: Design and analyze an approach to develop advanced signal analysis tools for utilization on non-periodic radio frequency (RF) signal sources. Evaluate candidate algorithms and validate the approach in a high-fidelity modeling and simulation environment. Include the development of models and simulations in order to validate the approach, demonstrate feasibility and reduce technical risk for Phase II. The Phase I effort will include prototype plans to be developed under Phase II.

 

PHASE II: Further refine and optimize the Phase I technical developments and implement algorithms and software into an embedded and GPC demonstration system for characterization of performance for detecting, processing, classifying and generating UWB, Noise Radars, and other signals. Develop a transition plan for Phase III.

 

Work in Phase II may become classified. Please see note in Description.

 

PHASE III DUAL USE APPLICATIONS: Support integration and demonstration of technology as a capability enhancement for the Airborne Electronic Attack (AEA) technology on the EA-18G (REAM FNC). Final testing would include demonstrating the suitability of any hardware and software for application into an airborne environment. Although the basic concepts and techniques that will be developed could advance numerous commercial applications, this effort is not intended for the private sector domain.

 

REFERENCES:

1.   Liu, Y., Zhang, X., and Yu, Y. �Classification of Vessel Targets Using Wavelet Statistical Features�. 5th International Congress on Image and Signal Processing. Chongqing, 2012.