Clipping, Channel Fading, and Multi-path Interference
Navy SBIR 2019.2 - Topic N192-128
ONR - Ms. Lore-Anne Ponirakis -
Opens: May 31, 2019 - Closes: July 1, 2019 (8:00 PM ET)


TITLE: Innovative Artificial Intelligence Features to Reduce Signal Dropout due to

Clipping, Channel Fading, and Multi-path Interference


TECHNOLOGY AREA(S): Electronics, Information Systems, Sensors ACQUISITION PROGRAM: Several Programs of Record are potential users.

The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with section 3.5 of the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws.


OBJECTIVE: Develop and demonstrate an Artificial Intelligence (AI) methodology or deep learning Digital Signal Processing (DSP) soft/firm-ware structure for signal recognition and reception that improves the data rate sustainable in the presence of clipping and strong fading, especially in cases where the fading has a periodic temporal structure.

DESCRIPTION: Movement of either endpoint of a communications link or changes in the multi-path scattering by the environment can force many mobile systems to cope with signals with strongly time dependent amplitude ("deep fading") on time scales of microseconds to seconds. Wideband systems are often built without analog clutter- automatic gain control and hence often experience clipping and/or small signal inadequacy. They are also especially bothered by multi-path fading since different carrier frequencies are impacted differently by the same changes in the reflector environment. Signal dropout within data links is thus common. Antenna diversity is often used to allow the stronger amplitude signal to be chosen at any given time. But this patch, at a minimum, doubles the hardware costs and has DSP back end complexity issues if the copies are not of perfectly identical quality. Additionally, it does nothing to solve the clipping issue. The need is for a methodology to cope in the back end with signals for which the correctness of the received data (e.g., the bit error rate) fluctuates in time. In many of these settings with longer dropped data intervals, the signal amplitude recovers quasi-periodically; reception can restart but a new link establishment protocol is often required to be run, lowering the time available for actual data before the next fade happens and lowering data throughput. Layered signal reception schemes appear to be needed. One might first process each time segment of signal of adequate amplitude to have at least a marginally acceptable bit error rate and estimate that segment of data to produce both value and accuracy/confidence estimates as part of a probabilistic interpretation. Once some number of intervals have been interpreted, attempts can be made to stitch together the successive intervals, for example, by using machine learning/AI techniques to improve the understanding of each segment by virtue of having the data available from the other time intervals. Methods could include working from both ends of two time segments in order to build up an image of signals by concatenating more and more "on" intervals. Consulting multiple disjointed temporal segments of the same underlying signal will allow reuse of the already collected data and refine our knowledge of the modulation and optimize error correction, while benefiting from a continuous time base and allowing adaptive equalization. This sort of real-time training that improves the continuity of receptions ought to reduce the volume of redundant data transmission required. The AI methodologies developed should be demonstrated using some form of commercial off-the-shelf (COTS) processor working in real time on a high-speed (e.g., > 20 GSps) digital data stream that represent a wide (e.g., >4 GHz) instantaneous bandwidth and in a manner consistent with the principles of open system architectures. Approaches that can work in dense signal environments having substantial spectral overlap between multiple simultaneous signals of substantially different magnitude are especially desirable. Performance should be measured against the case of stationary Rx and TX nodes and a stable communications link between them.


PHASE I: Define at most two approaches that will be developed and tested during the Phase I base period. Provide test cases that start with an intentionally clipped signal and prove that for increasing levels of signal distortion, the Bit Error Ratio (BER) is preserved to higher distortion and longer gaps in highly accurate data with the new technique employed than not. When progress warrants, move on to a representative stored data set that includes: a) signal densities high enough that in the time domain, the total signal is describable as displaying interference noise, or b) more standard 1 and 2 tone tests, first without, then with periodic fading. By the end of Phase I, document that the success of the executed tests is not dependent on any special relationship between the periodicity of the fading and the signal carrier or modulation. During the option, explore issues not addressed in the base, including documenting independence of the success on receiver sample rate and bit depth of the analog to digital converter (ADC). Prepare a Phase II plan.


PHASE II: Develop the Phase I results into a prototype system implementation, including application to a wideband data stream that is to be processed for specific signal detection in real time. Demonstrations that a signal with a set of specific, a priori known baseband waveforms can be located anywhere in a wideband spectrum response by the developed methodology are particularly desired. Deliver the implementation hardware and the software source code developed under Phase II at the end of the effort.


PHASE III DUAL USE APPLICATIONS: The DoD transition path would lead into back end digital processors that support wideband electronic support (ES) receivers and provide situational awareness. The commercial applications would focus on signals enhancement in mobile applications (especially in cars in heavy traffic and planes near airports) and antennas in general. Signal fading in specific disadvantaged locations could be mitigated, for example helping to cope with GPS signal drop out in urban canyon contexts. In rural settings, the reception range would be enhanced since the integrative methods requested ought to decrease the required signal-to-noise ratio for successful signal reception.


1.   Russell, Stuart J., and Peter Norvig, Artificial intelligence: A Modern Approach, Third Edition. Pearson Education Limited, Essex, England, 2016.


2.  Nilsson, Nils J. Principles of Artificial Intelligence. Morgan Kaufmann, Burlington, MA, 2014.


3.   Bkassiny, Mario, Li, Yang, and Jayaweera, Sudharman K. "A Survey on Machine-learning Techniques in Cognitive Radios." IEEE Communications Surveys

& Tutorials 15.3 (2013): 1136-1159.


KEYWORDS: RF Signal Capture; Signal Fading; Antenna Diversity; Interference Temperature; Artificial Intelligence; AI; Integrative Signal Processing; Specific Signal Detection



Deborah Van Vechten




Steven Henry




These Navy Topics are part of the overall DoD 2019.2 SBIR BAA. The DoD issued its 2019.2 BAA SBIR pre-release on May 2, 2019, which opens to receive proposals on May 31, 2019, and closes July 1, 2019 at 8:00 PM ET.

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