Adaptive Control of Digital Channelized Receivers

This effort investigates techniques for adaptive control, dynamic allocation and reconfiguration of digital channelized receivers for signal detection and characterization of widely varying signals. Several approaches to detect and estimate the signal instantaneous modulation bandwidth are considered to enable signals to be dynamically allocated to digital measurement receivers with sub-band tuning, bandwidth, sample rate and demodulation parameters specified. One potential approach uses a polyphase implementation with multiple channelization stages and arbitration. A second employs entropy based detection processes to localize signal extent. The third, employing dyadic Wavelet decomposition to channelize in a structured fashion, is included for its potential efficiency in covering large time-frequency signal space while providing finer grain bins in either time or frequency to address narrow pulses and narrowband signals respectively. A final approach, the Fractional Fourier Transform(FrFT) is considered for its potential to detect and characterize signals in a maximally compact fractional domain. Previous MATLAB and FPGA code for feature extraction, polyphase channelizer, entropy-based signal detection and FrFT are leveraged. An existing digitized signal library (real world and synthetic) are proposed for testing. The effort defines an initial implementation and assesses feasibility for use in COTS VME FPGA boards (e.g. Annapolis Microsystems WILDSTARTM 5). BENEFIT: Key benefits and findings developed during this effort are applicable to any ES or ELINT application where signals must be detected and characterized rapidly. They will be able to be easily applied to other DoD COMINT or MASINT systems. Key benefits of the proposed approach include: 1) Novel approaches for initial detection and modulation bandwidth estimation maximizes SNR and resource allocation for subsequent signal characterization 2) Adaptable– sample rate, center frequency, bandwidth, filters and demodulation processes can be tailored to: a. Address the estimated signal bandwidth and time extent b. Utilize a priori knowledge for specific signals of interest for ELINT and/or ISR applications 3) Reconfigurable -Utilizes COTS NDI hardware with state-of-the-art FPGAs 4) Maximizes Re-use – a. Leverages significant research and development for AFRL on entropic processing, complex IMOP characterization, and stressing FMCW and very narrow pulse processing waveform detection on projects such as “Automatic Processing for Wideband” b. Leverages several MATLAB models and FPGA designs as building blocks c. Utilizes FPGA cores from the NAVSEA ES-PFEP Phase II SBIR, Phase III for NAVAIR PMA-265 and ARMY CESAD-SET Proof of concept (polyphase channelizer with precision digital set-on receivers). The processing enhancements proposed herein have numerous military and commercial applications. Potential ELINT applications include systems such as the RIVET JOINT Manual Station and multiple UAV programs where the two card COTS ELINT package provide a significant benefit whereby adaptable and reconfigurable algorithms and processing would enable the payload to be tailored to the theater and/or mission. A strong potential candidate for an ES system application is the AF B-2 Defense Management System (DMS) Upgrade; this program is slated for a 2010 RFP. Other relevant applications include the JSF digital receiver and possible future programs for F-16 Block Upgrades pending on AF long term plans. Several candidate NAVY applications have been identified as well. These include the NAVSEA Surface EW Improvement Program Block II (SEWIP) with a digital receiver and precision measurement based on COTS hardware and FPGA processing now undergoing contractor evaluation down-selection, the digital receiver upgrade to ALR-67(v3), and the PMW-180 Ship’s Signals Exploitation Equipment (SSEE) Increment F. Potential applications in the private sector include wireless waveform characterization and communication waveform fidelity assessment and classification to assist for mapping spectral interference for adaptive spectrum allocation. These will be explored in more detail in Phase I and Phase II.