Table Of Contents

12. Applications of Cyclostationarity to Signals Intelligence

  • 12.1 Descriptive Material on SSPI

    Compared with the Nation’s largest defense contractors, the creativity and productivity in development of theory, method, algorithms, and software of the tiny R&D firm, SSPI
    (Statistical Statistical | adjective Of or having to do with Statistics, which are summary descriptions computed from finite sets of empirical data; not necessarily related to probability. Signal Processing, Inc.), in the field of signals intelligence is nothing short of phenomenal. The SSPI team was comprised of fewer than ten employees, most of whom were trained by Professor Gardner, SSPI’s president and chief scientist, as PhD students and post-doctoral researchers at the University of California, Davis. The entire budget of SSPI over its 25-year lifetime was less than $25M. The company’s huge store of intellectual property was sold (in essence, gifted) in 2011 to Lockheed Martin Corporation at the time of Gardner’s retirement for a small fraction of this sum and an agreement for placement of SSPI’s senior engineers at the Lockheed Martin Advanced Technology Center in Palo Alto, California.

    The following images were excerpted from a 2003 SSPI PowerPoint presentation. They provide a concise overview of some of the technology that was developed by SSPI during the first 15 years of its 25-year history from 1986 to 2011. Following this presentation is an in-depth summary of SSPI’s technology development during its final 10 years. Also available herein on Page 6 is an in-depth survey of SSPI’s early work on application of cyclostationarity theory and method to the specialized field of signal interception. This survey was written 5 years after the 1987 publication of the enabling book [Bk2]. 

    2010 Summary of SSPI’s Technology Development Activities

    Following is an outline of SSPI’s developed technology in the area of communications signal processing specifically for unintended receivers, as of the end of 2010. The great majority of SSPI’s technology is in the form of technical documents and software (scientific/engineering documentation of innovative theory, methodology, algorithms, software implementations of individual signal processing algorithms and data processing systems of such algorithms, and performance-evaluation data and analysis). These theoretical and methodological results were developed without outside funding. The sources of funding include investments-in-kind of the Owner’s labor, SSPI IRAD funding not billed to any clients as an overhead expense, and some SSPI IRAD funding billed as an overhead expense to contracting customers.

    OUTLINE
        1. RF-EMITTER GEOLOCATION
          1. Novel (and deeper than state-of-the-art) theoretical problem reformulation and solution for long-coherent-integration for fixed or moving ground-based (or low-altitude) RF-Emitter detection/location from primarily aircraft and satellites, based on:
            1. High-fidelity mathematical modeling of
              1. Communications signals of interest
              2. Collection uplink/downlink channels, uplink transmit/receive antennas, multi-path propagation, blocked line of sight
            2. Mathematical optimization of statistical performance criteria for geolocation of known-, partially-known-, and unknown-signal emitters
            3. Solution for novel explicit statistical location-performance-reporting and performance-prediction formulas
          2. Translation of mathematical solutions into novel signal-processing algorithms, implementable in S/W & H/W, configurable according to application and scenario
          3. Translation of mathematical solution into a novel theory of statistically optimum, passive, evolutionary Bayesian Aperture Synthesis for Emitter Location (BASEL), including novel engineering concepts:
            1. Posterior Probability Density Images and Likelihood Images in three dimensions
            2. Formulas for RF Imaging Point-Spread Functions for amplitude and energy
          4. Mathematical unification, as well as extension/generalization, of diverse technologies into a single cohesive methodology that includes:
            1. Optimum Joint multi-sensor TDOA/FDOA/AOA estimation
            2. VLBI types of RF Imaging from overhead
            3. Signal Selective RF Imaging
            4. RF Imaging that suppresses reflector positions
            5. RF Imaging that effectively sees through blocked line of sight
          5. Beta prototype S/W for configurable processor that performs long-coherent-integration geolocation and performance prediction for fixed emitters

       

      1. AUTOMATIC RF-SIGNAL DETECTION & MODULATION CLASSIFICATION
        1. Novel cyclostationarity-exploiting algorithms and underlying theory for signal detection and estimation
        2. Cochannel-interfering-signal classification algorithms and theory
        3. Beta prototypes of algorithms in S/W, plus extensive documentation
      2. JOINT RF-SIGNAL DEMODULATION
        1. Novel extensions/generalization of Viterbi algorithms for cochannel signals
        2. Novel Viterbi algorithms for cochannel DQAM signals with distinct constellations and symbol rates
        3. Beta prototypes of algorithms in S/W, plus documentation
  • 12.2 Key Roles Played by Academic Institutions

    Reaching back farther into the early 1980s, in connection with my work on exploiting cyclostationarity to despread a Direct-Sequence Spread Spectrum signal without using the spreading code (eventually published in the paper [JP14]), Professor Herschel H. Loomis of the Naval Postgraduate School (NPS) in Monterey, California, brought to my attention a class of signal processing problems needing solutions which he suggested may be amenable to techniques based on exploitation of cyclostationarity. This class of problems, referred to as Signal Interception, belongs to the broader field of what is called Signals Intelligence. The focus of applications of my work on cyclostationarity was originally commercial communications systems, a natural outgrowth of my pre-doctoral work at Bell Telephone Laboratories. The signals intelligence problems that SSPI addressed involved primarily radio frequency wireless communications systems, both commercial and military, but was distinct from my earlier work in that the problems addressed arose from the perspective of unintended receivers wanting to extract information from received signals that would be of value for gathering intelligence for the purpose of national defense.

    This gave rise to a collaboration that led to the expansion of my modest consulting services to commercial industry into an incorporated research and development firm, SSPI (Statistical Statistical | adjective Of or having to do with Statistics, which are summary descriptions computed from finite sets of empirical data; not necessarily related to probability. Signal Processing Inc.), consisting initially of a group of M.S. and Ph.D. students working on thesis projects at the University of California, Davis, and later expanding to include post-doctoral employees from UCD and elsewhere. In parallel with this development, there was the development of an academic group at NPS, under the direction of Professor Loomis. Whereas SSPI focused on the development of theory and method for tackling signals intelligence challenges, the NPS group focused more on evaluating specific techniques suggested by the theory and associated methodology. This synergistic relationship continued into the early 1990s. Following the first workshop on cyclostationarity in 1992, held in SSPI’s hometown of Yountville, California, and co-sponsored by four independent funding agencies, the National Science Foundation and the Offices of Research of the Army, Navy, and Air Force, SSPI’s customer base grew substantially. Although this detracted from the extent of collaboration between SSPI and NPS, the NPS effort on evaluating cyclostationarity-exploiting techniques also continued to grow, as illustrated in the chronological list of NPS thesis projects included below, which covers the 26-year period from 1983 to 2009.

    In addition to UCD and NPS, a key role in applications to signals intelligence was also played by the AFIT (US Air Force Institute of Technology), and other institutions as briefly discussed on Page 6.

    UCD Thesis Projects

    • Ph.D. Daniel Bukofzer “Coherent and noncoherent detection of cyclostationary signals in cyclostationary noise.” 1979
    • M.S. Catherine French “Spread Spectrum despreading without the code.” 1984
    • M.S. Chinkang Chen “Spectral correlation of modulated signals.” 1985
    • Ph.D. William Brown “On the theory of cyclostationary signals.” 1987
    • M.S. Stephan Schell “Self-coherence restoral (SCORE): A new approach to blind adaptation of antenna arrays.” 1987
    • M.S. Chad Spooner “Performance evaluation of detectors for cyclostationary signals.” 1988
    • Ph.D. Randy Roberts “Digital architectures for cyclic spectral analysis.” 1989
    • M.S. Robert Calabretta “On cyclic MUSIC algorithms for signal-selective direction estimation.” 1989
    • Ph.D. Brian Agee “The property restoral approach to blind adaptive signal extraction.” 1989
    • Ph.D. Chihkang Chen “Spectral correlation characterization of modulated signals with application to signal detection and source location.” 1989
    • M.S. Teri Archer “Exploitation of cyclostationarity for identifying the Volterra kernels of a nonlinear system.” 1990
    • Ph.D. Stephan Schell “Exploitation of spectral correlation for signal-selective direction finding.” 1990
    • Ph.D. Chad Spooner “Theory and application of higher-order cyclostationarity.” 1992
    • M.S. Grace Yeung, “New methods of cycle detection.” 1993
    • M.S. Peter Murphy, “Performance evaluation of a blind adaptive antenna array in cellular Communications for increasing capacity.” 1993
    • M.S. Gene Fong, “Evaluation of least-squares algorithms for detection and estimation of cyclostationary signals.” 1993
    • M.S. Jeffrey Schenck, “Evaluation of a method for blind adaptive spatial processing.” 1994
    • M.S. Kurt E. Sundstrom, “Time-variant filtering for GSM signal separation.” 1998
    • Ph.D. Mathew A. Mow, “Periodically-time-variant spatio-temporal filtering for improvement of GSM networks.” 1998

    NPS Thesis Projects

    • “Digital Implementation of Cyclic Spectrum Analysis Techniques for the Detection of Signals in Noise,” William Roscoe Tucker, LT USN, Master of Science in Electrical Engineering, September 1983
    • “Detection of Spread Spectrum Communications,” C. A. Laurvick, LCDR USN, Master of Science in Electrical Engineering, June 1984
    • “Performance Evaluation of the S-1 MkIIA Uniprocessor in Conducting Digital Cyclic Spectral Analysis,” Mark Worthington Hartong, LT USN, Master of Science in Computer Science, June 1985, (C)
    • “VHSIC Implementation of Cyclic Spectrum Analysis Algorithm,” Charles Leonard Kanewske, LT USN, Master of Science in Electrical Engineering, June 1985
    • “Detection of Randomly Clocked Direct Sequence Spread Spectrum Signals,” Stephen Fox, Cpt USA, Master of Science in Electrical Engineering, March 1986
    • “Detection of Spread Spectrum Signals in the Presence of Noise and Interference,” Valdemar K. Johnson, LCDR USN, Master of Science in Electrical Engineering, March 1986
    • “On the Theory of Cyclostationary Signals,” William A. Brown, III, Doctor of Philosophy, Electrical Engineering, University of California, Davis, September 1987, (Member of Guidance Committee)
    • “Interference Removal in Cyclic Spectral Analysis,” Charles Rowe, LCDR USN, Master of Science in Electrical and Computer Engineering (Space Engineering), September 1987.
    • “Cyclic Spectral Analysis Architectures,” Curtis Mitchell, LCDR USN, Master of Science in Weapons Engineering, December 1987.
    • “Design and Analysis of a Covert Communications System,” Richard Lockowitz, LT USN, Master of Science in Systems Technology (Telecommunications Systems Management), September 1988.
    • “Microcomputer Implementations of Spectral Correlation Algorithms,” Thomas V. Cole, LT USN, Master of Science in Electrical Engineering, September 1988.
    • “Geolocation of Direct Sequence Spread Spectrum Signals,” Michael Loomis, LT USN, Master of Science in Electrical Engineering, December,1988.
    • “Design and Implementation of a Receiver for Detecting a Multifrequency Quaternary Phase Shift Keying Signal on an Industry Standard Computer,” Terry K. Gantenbeim, LT USN, Master of Science in Electrical Engineering, June 1989, (R)
    • “Detection of Frequency-Hopping Communications Signals,” Gregory F. Mansfield, LCDR USN, Master of Science in Electrical Engineering, June 1989.
    • “Architectures for Digital Cyclic Spectral Analysis,” Randy S. Roberts, Doctor of Philosophy in Electrical Engineering and Computer Sciences, University of California, Davis, CA, September 1989, (C).
    • “Despreading of Spread Spectrum Signals,” Gregory Point, LT USN, Master of Science in Electrical Engineering, June 1990, (C).
    • “Spread Spectrum Implications in Radar,” William A. Hartung, LT USN, Master of Science in Systems Technology (Space Systems Operations), September 1990.
    • “Feasibility of Two Hypothetical Covert Satellite Communications Systems,” Kim, Hong C., LT USN, Master of Science in Systems Technology (Space Systems Operations), Sept. 1991.
    • “A VLSI Design of a Radix4 Floating Point FFT Butterfly,” Zimmer, Michael, LT USN, Master of Science in Electrical Engineering (Space Systems Engineering), Dec. 1991.
    • “Geolocation using a Cyclostationary Time Difference of Arrival Technique,” Timothy A. Benson, LT USN, Master of Science in Electrical Engineering, December 1992, (C).
    • “Design of Spectral Correlation Analyzer Software,” Nancy Carter, LCDR USN, Master of Science in Electrical Engineering, December 1992, (C).
    • Jackson, Kevin L., LCDR USN, “A CMOS VLSI Implementation of a Near Real Time FFT,” Master of Science in Electrical Engineering, September 1994.
    • Radcliffe, Roy M., LCDR USN, “Detection of Spread Spectrum Signals Using Cyclic Spectral Analysis Techniques” Master of Science in Electrical Engineering, December 1994.
    • Bernstein, Raymond, “A Pipelined Vector Processor and Memory Architecture for Cyclostationary Processing,” Ph.D. in Electrical Engineering, December 1995.
    • Jenik, Douglas A., LT USN, “Time-Difference-of-Arrival Estimation Using Cyclostationary Signal Processing Techniques, Parts I & II,” Master of Science in Electrical Engineering, March 1996
    • David Streight, LT USN, “Application of Cyclostationary Signal Selectivity to the Carry-on Multi-platform GPS Assisted Time Difference of Arrival System, ” Master of Science in Electrical Engineering, March 1997
    • Strozzo, Phillip G., LCDR USN, “Detection and Classification of Digital Communications Signals Using Second- and Higher Order Cyclostationary Features (Parts I & II),” Master of Science in Electrical Engineering, June 1998 (C).
    • Mateo, Niels, LT USN, “The Effects of Time Varying Doppler on Cyclic Spectral Analysis,” Master of Science in Electrical Engineering, December 1998 (C).
    • Streight, David, LT USN, “Maximum-Likelihood Estimators for the Time and Frequency Differences of Arrival of Cyclostationary Digital Communications Signals,” Doctor of Philosophy in Electrical Engineering, June 1999.
    • Antonio F. Lima Jr., Captain, Brazilian Air Force, “Analysis Of Low Probability Of Intercept (LPI) Radar Signals Using Cyclostationary Processing,” Master of Science in Systems Engineering-September 2002, http://theses.nps.navy.mil/02sep_Lima.pdf (C)
    • Pedro F. Jarpa, Captain, Chilean Air Force, “Quantifying The Differences In Low Probability Of Intercept Radar Waveforms Using Quadrature Mirror Filtering,” Master of Science in Electrical Engineering-September 2002,  http://theses.nps.navy.mil/02sep_Jarpa.pdf (C)
    • Crnkovich, Joseph G., “Efficacy of Various Waveforms to Support Geolocation,” MSEE, June 2009 (C)
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