N112-161 TITLE: Exploiting Agile Waveforms and Sampling for Compressive Sensing Radar

TECHNOLOGY AREAS: Information Systems, Sensors

RESTRICTION ON PERFORMANCE BY FOREIGN CITIZENS (i.e., those holding non-U.S. Passports):  This topic is "ITAR Restricted."  The information and materials provided pursuant to or resulting from this topic are restricted under the International Traffic in Arms Regulations (ITAR), 22 CFR Parts 120 - 130, which control the export of defense-related material and services, including the export of sensitive technical data.  Foreign Citizens may perform work under an award resulting from this topic only if they hold the "Permanent Resident Card", or are designated as "Protected Individuals" as defined by 8 U.S.C. 1324b(a)(3). If a proposal for this topic contains participation by a foreign citizen who is not in one of the above two categories, the proposal will be rejected.

OBJECTIVE: Identify and demonstrate enhanced, efficient radar imaging and applications exploiting modern waveform generation and/or MIMO beam formation for robust compressive sensing in maritime applications such as small craft detection and imaging.

DESCRIPTION: Compressive Sensing has emerged in recent years as a potentially feasible mathematical tool and framework for efficient data collection. However, few active air-to-surface surveillance systems currently exploit the full potential of compressive sensing by probing the ground with fully randomized or partially randomized waveforms. While complete randomization may be impractical, modern digital radar systems are capable of synthesizing a wide variety of IF modulating waveforms, enabling a more complete and efficient exploration of the time-frequency-angle space over the radar system�s path than is feasible with current methods. Similarly, MIMO radar systems could introduce randomized beam patterns that introduce variations in the angular distribution of signal over targets in each range bin. Finally, an additional layer of randomization and efficient data reduction may be achieved digitally on board the aircraft by weighting and combining pulses over the aperture path before downlink.

A successful program on this topic will need to examine and address several significant challenges. One must examine the capabilities and limitations of radar systems using direct waveform synthesis and/or MIMO radar systems to understand the waveforms that can practically be produced and how robustly and reproducibly they can be created. Then, randomized waveform generation may need to be optimized to ensure robust coverage of the possible signal spaces. In addition, while much current work on compressive radar sensing focuses on reconstructing isolated sparse scatterers, which may be useful for periscope or small craft imaging, additional improvements may be possible for reducing the impact of high-sea state clutter by better filtering sea-clutter signal in appropriate compressed-sensing signal bases. Sensor noise, interference, and errors in signal generation (particularly deviations in MIMO beamshapes from what was intended) represent potentially significant confounding factors.

Proposals should address some of the core practical signal processing issues for such a system. For example, proposed approaches should ensure sufficient coverage of the signal space. They should also examine practical waveform generation and measurement. Quality measures for the output reconstruction should be considered, as well as assessments for achieving those requirements. Also, downlink requirements that include sufficiently describing the transmitted waveforms to the ground station in order to ensure accurate reconstruction. Robustness to interference and error sources in both waveform generation and receiving should be considered.

Systems using such approaches will likely be capable of simultaneous multi-mode operations and make more efficient use of their data downlink. In addition we anticipate that a compressive radar sensing system exploiting randomized waveforms could be more robust to interference (unintentional and intentional) and would minimally interfere with other EM operations (military or civilian). As an additional side benefit, such a system could be stealthier and more difficult to counter, because of its randomized, spread spectrum characteristics.

PHASE I: Determine the requirements of a compressive sensing radar system using modern agile waveform synthesis and/or MIMO beamshaping capabilities. Illustrate the capabilities and limitations of the framework in simulation for proof-of-concept. Assess performance and robustness to interference and error sources.

PHASE II: Develop waveform generation and reconstruction prototypes using compressive sensing techniques. Demonstrate the approach in an appropriate simulation, including data collection if feasible and assess the performance of techniques on previously collected data.

PHASE III: Affordable implementation appropriate to a specific system which program officer will identify in the course of phase II execution.

PRIVATE SECTOR COMMERCIAL POTENTIAL/DUAL-USE APPLICATIONS: Many civil and military systems that depend upon electromagnetic sensing can benefit from significant advancement of the state of the art. Robust, efficient all-weather persistent standoff imaging that is robust to interference will significantly enhance the capability of the warfighter to execute actions and gather intelligence in all environments. Efficient and flexible data transfer utilization can free spectrum for alternate uses. Satellite imaging capabilities can be significantly enhanced. Significant resource reductions could imply cost reductions, enabling more widespread uses of RF imaging and sensing. Any radar system utilizing a random or less predictable pattern of emissions will likely challenge exploitation opportunities. Alternatively, it is conceivable that many of the compressive sensing and imaging techniques developed here could also be used to enhance minimally invasive medical imaging, exposing patients to less radiation by requiring fewer probing sources.

REFERENCES:
(1) S. Shah, Y. Yu, and A. Petropulu, "Step-Frequency Radar with Compressive Sampling (SFR-CS)," in Proc. ICASSP 2010, March 2010

(2) Herman, M.A.; Strohmer, T.; , "High-Resolution Radar via Compressed Sensing," Signal Processing, IEEE Transactions on , vol.57, no.6, pp.2275-2284, June 2009

(3) M. D. Gabriel Rilling and B. Mulgrew, "Compressed sensing based compression of SAR raw data," in SPARS�09 - Signal Processing with Adaptive Sparse Structured Representations (2009), 2009

(4) C. Y. Chen and P. P. Vaidyanathan, "Compressed sensing in MIMO radar," Proc. 42nd Asilomar Conf. Signals, Syst. Comput, Pacific Grove, CA, Nov. 2008.

(5) Subotic, N.S.; Thelen, B.; Cooper, K.; Buller, W.; Parker, J.; Browning, J.; Beyer, H.; "Distributed RADAR waveform design based on compressive sensing considerations," Radar Conference, 2008. RADAR '08. IEEE , vol., no., pp.1-6, 26-30 May 2008

(6) Bhattacharya, Sujit; Blumensath, Thomas; Mulgrew, Bernard; Davies, Mike; "Fast Encoding of Synthetic Aperture Radar Raw Data using Compressed Sensing," Statistical Signal Processing, 2007. SSP '07. IEEE/SP 14th Workshop on , vol., no., pp.448-452, 26-29 Aug. 2007

(7) Baraniuk, R.G.; "Compressive Sensing [Lecture Notes]," Signal Processing Magazine, IEEE , vol.24, no.4, pp.118-121, July 2007

(8) Donoho, D.L.; "Compressed sensing," Information Theory, IEEE Transactions on , vol.52, no.4, pp.1289-1306, April 2006

(9) Donoho, D. L., Elad, M., "Optimally sparse representation in general (nonorthogonal) dictionaries via L1 minimization," Proc. Natl. Acad. Sci. USA 100 (2003), 2197�2202.

KEYWORDS: Compressive Sensing, Radar, SAR, Clutter Mitigation, MIMO, Direct Waveform Synthesis

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