N092-138 TITLE: EW Parametrics for Improved Emitter Classification/Identification

TECHNOLOGY AREAS: Sensors

ACQUISITION PROGRAM: PEO IWS 2.0 Surface Electronic Warfare Improvements Program, ACAT II

The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), which controls the export and import of defense-related material and services. Offerors must disclose any proposed use of foreign nationals, their country of origin, and what tasks each would accomplish in the statement of work in accordance with section 3.5.b.(7) of the solicitation.

OBJECTIVE: Techniques to increase the number of emitter parameters measured by shipboard ESM systems are being introduced to the fleet. These techniques effectively measure higher orders of detail than has been heretofore measured. However, theses new parameters are subject to higher orders of distortion from environmental or propagation effects as well. The objective of this topic is to reduce or eliminate the effects of these distortions.

DESCRIPTION: Navy ships currently employ passive Electronic Support (ES) to detect and classify/identify Radio Frequency (RF) emitters as key information used to support platform Situational Awareness. ES processing requires extracting emitter pulse train data information from the intercepted emitters that provide parametric data that is compared to similar formatted (library) data from expected or known emitters to obtain either type classification or unique ID. Because current emitter data is derived from classical measurements using a minimum number of parameters, it is not unusual to get several possible ID candidates. Techniques to increase the number of measured emitter parameters thereby reducing or eliminating the above mentioned ambiguities are being introduced to the fleet. These techniques effectively measure higher orders of detail than have heretofore been measured. Theses new parameters however are subject to higher orders of distortion from environmental or propagation effects, such as multipath and tropospheric scattering therefore making their exploitation problematical with current analysis techniques. Some work has been done that indicates that the distortions produced are measurable and quantifiable and that new techniques and theory may exist which can reduce or eliminate them. These emerging techniques include the use of bi-spectral distributions, principle and independent component analysis, cepstrums, higher order and fractional order statistics, etc. The US Navy�s ability to maintain situational awareness of the battle space will be degraded if this type of research is not conducted and then developed into a useable analysis process.

PHASE I: The awardee shall research and identify advanced processing and measurement techniques that extract emitter characteristics that provide unique information that allows for the mitigating of environment and propagation effects on the measurement of higher order emitter parameters.

PHASE II: The awardee shall develop, document, and model proposed processing techniques that improve the reliability and robustness of emitter classification, reduction of ambiguities, or that provide unique identification regardless of the propagation environment. Results of analysis, model testing, and research shall be documented with specific techniques proposed for follow-on development, testing, and validation. Documentation shall include sufficient detail to support follow-on ES development efforts such as SEWIP Block 2.

PHASE III: The awardee shall reduce development risk and mature the development of promising processing techniques and conduct analysis, testing and evaluation using modeling and simulation as well as field testing on prototype algorithms to ascertain the maturity of the techniques selected. Processing techniques shall be sufficiently mature to support transition to programs that will field this capability for Fleet use. Classified data may eventually be required as specific processing techniques are evaluated.

PRIVATE SECTOR COMMERCIAL POTENTIAL: Software, processes, and algorithms developed for this effort could be used in any application where unique characteristics of information is needed to uniquely isolate or characterize information into specific areas.

REFERENCES:
1. "Higher-Order Spectral Analysis: A Nonlinear Signal Processing Framework" C. L. Nikias, A. P. Petropulu, Prentice Hall, Englewood Cliffs, NJ, USA (1993).

2. "Signal Processing with Alpha-Stable Distributions and Applications" C. L. Nikias, M. Shao, Wiley-Interscience, New York, NY, USA (1995).

3. "Adaptive Blind Signal and Imaging Processing: Learning Algorithms and Applications" A. Cichocki, S. Amari, Wiley, New York, NY, USA (2002).

4. "High-accuracy, Low-ambiguity Emitter Classification Using an Advanced Dempster-Shafer Algorithm" PROCEEDINGS-SPIE THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING, D. C. Black, J. C. Sciortino Jr., J. R. Altoft. (2004).

KEYWORDS: Electronic Library; Signal Processing; Data Mining

** TOPIC AUTHOR (TPOC) **

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