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Integrated High-Frequency Analog-to-Information Receiver


RT&L FOCUS AREA(S): General Warfighting Requirements


OBJECTIVE: Develop and demonstrate a compressive sensing receiver fabricated from photonic integrated circuits (PIC) that can recover arbitrary Radio Frequency (RF) signals in the K (18-27 GHz), Ka (27-40 GHz), V (40-75 GHz), and W (75-110 GHz) bands.

DESCRIPTION: Few Analog-to-Digital Converters (ADCs) are commercially available which can digitize RF signals up to 100 GHz, such as the LeCroy 10-Zi-A at 240 GS/s. However, these ADCs are designed to be laboratory test instruments, and as such are bulky (several cubic feet), have high-level power consumption (hundreds of watts), have a low effective number of bits (ENOB), and generate data at such a high rate that it cannot be transmitted to the ground from a remote platform. For example, the 240 GS/s ADC with 8 read-out bits has a data rate of 1.92 TeraBits Per Second (Tbps). While extensive work on photonic ADCs over the past 4 decades has shown progress towards higher data rate ADCs with higher ENOB and compact form by leveraging photonic integration [Ref 1], the systems are still impractical for remote platforms due to the high data rate. Compressive sensing (CS) provides an alternative solution for detecting wide bandwidth sparse signals [Refs 2-4]. In CS, the input RF signal is mixed down in dimension through an analog measurement matrix (MM) and Nyquist sampling rate is replaced by sampling random projections of the wideband signal at a rate on the order of the sparsity of the RF signal.

An implementation is sought of a high-performance, integrated, photonic solution to analyze RF signals in real time over a wide-frequency range, acquiring basic properties such as frequency, phase, and amplitude, or signal-specific properties such as chirp rate, pulse location, or modulation format. This will establish the technical and commercial foundation for the development of a fully integrated compressed-sensing system for direct RF-to-information conversion. One possible solution is based on interrogation of optical speckle in multimode planar waveguides, an ultra-broadband signal processing capability (i.e., taking advantage of the large signal bandwidth inherent in optical systems), integrated with a broadband pulsed optical source [Ref 5]. The integrated module will be specifically targeted to form an integrated RF receiver with the capability of resolving arbitrary properties (including, but not limited to, frequency, phase, and amplitude) of RF signals without direct digitization at the Nyquist rate with the following performance objectives: (a) real-time signal monitoring of 0.5-40 GHz RF spectrum (extendable to 100 GHz); (b) compact and ruggedized implementation,

PHASE I: Design, develop, and demonstrate feasibility for a fully integrated photonic integrated, circuit (PIC)-based, compressive sensing system. The Phase I effort will include prototype plans to be developed under Phase II.

PHASE II: Perform detailed development of the prototype and demonstrate it in terms of operational feasibility. Develop necessary PICs and integrate in a module to demonstrate and validate the PIC-based, compressive sensing system. Complete bench top integration and characterization to include a comparison of results to design objectives.

PHASE III DUAL USE APPLICATIONS: Complete development, perform final testing, and integrate and transition the final solution into naval aviation operational environments. Finalize the design for desired PIC performance and satisfying volume constraints. Demonstrate in naval aviation operational environments. PIC-based compressive sensing in a low size, weight and power configuration would be of benefit to multiband commercial satellites.


  1. Valley, G.C. “Photonic analog-to-digital converters.” Optics express, 15(5), 2007, pp. 1955-1982.
  2. Donoho, D.L. “Compressed sensing.” IEEE Transactions on Information Theory, 52(4), April 3, 2006, pp. 1289-1306.
  3. Cand├Ęs, E.J. and Wakin, M. B. “An introduction to compressive sampling.” IEEE Signal Processing Magazine, Volume 25, Issue 2, March 21, 2008, pp. 21-30.
  4. Baraniuk, R.G. “Compressed sensing.” IEEE Signal Processing Magazine, Volume 24, Issue 4, July 2008, p. 118.
  5. Sefler, G.A.; Shaw, T.J. and Valley, G.C. “Demonstration of speckle-based compressive sensing system for recovering RF signals.” Optics Express, Volume 26, Issue 17, 2018, pp. 21390-21402.
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