Enhanced Aircraft Non-Cooperative Target Recognition

Navy SBIR 23.1 - Topic N231-021
NAVAIR - Naval Air Systems Command
Pre-release 1/11/23   Opens to accept proposals 2/08/23   Closes 3/08/23 12:00pm ET    [ View Q&A ]

N231-021 TITLE: Enhanced Aircraft Non-Cooperative Target Recognition

OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): General Warfighting Requirements (GWR)

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 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 Doppler-polarimetric-based Non-Cooperative Target Recognition (NCTR) techniques as a complementary enhancement to legacy NCTR techniques.

DESCRIPTION: The ability to generate and exploit information from multiple polarizations generally has not been possible with our fielded airborne maritime surveillance and air-to-air radar systems due to their single polarization capability. Some of the newest generation of air-to-air and airborne early warning radar system are fully polarimetric, so there is no physical reason not to pursue this new source of information if meaningful performance improvements can be realized. Long ago, polarimetric radar was shown to be valuable in civilian applications including: (a) agriculture, for crop-type identification, crop condition monitoring, soil moisture measurement, and soil tillage and crop residue identification; (b) forestry, for clear-cuts and linear features mapping, biomass estimation, species identification and fire scar mapping; (c) geology, for geological mapping; (d) hydrology, for monitoring wetlands and snow cover; (e) oceanography, for sea ice identification, coastal wind field measurement, and wave slope measurement; and (f) coastal zone, for shoreline detection, substrate mapping, slick detection and general vegetation mapping. Many of these uses are also of value to the military. However, there are other potentially valuable benefits of polarimetry. These include improved performance in the presence of rain, using polarization selectivity/diversity to counter effects from jammers, and improved non-cooperative target recognition (NCTR) capability, particularly when used to enhance with traditional techniques such s High Range Resolution(HRR), Inverse Synthetic Aperture Radar (ISAR), micro-Doppler and Jet Engine Modulation (JEM). While the information content in polarimetric variables may be limited, it will be available under the constraints on time, carrier frequency, and bandwidth, as long as the system allows for multiple polarizations. This makes polarimetric features especially interesting for target classification.

Of particular interest in this SBIR topic is the utility of polarimetry in the characterization or classification of electrically-large, nonstable targets. The question of whether polarimetric data can enhance target classification has been touched on to some degree in the open literature. We know that polarization scattering properties are, in general, invariant for targets of interest, but they may vary widely and rapidly for only small aspect changes. Even for the simple extended targets, no well-defined optimum polarization exists. These targets are large in comparison to the wavelength, and have unresolved scattering centers. Because of the internal movements of the scattering centers, or due to changes of the aspect angle with the radar, the relative distances between scattering centers changes and thus the scattering properties of targets. It is shown that compound objects can be represented as a set of deterministic scatterers by using Doppler polarimetric formalism. In some respects, Doppler polarimetry can be considered as a decomposition of a random target into deterministic ones. The Doppler polarimetric decomposition is based on the spectral properties of targets and the result is more physical than for the decomposition theorems, which are based on only polarimetric targets properties. A variety of target recognition approaches are possible. Consider a polarimetric ISAR target image. It could be broken down into a set of scattering centers. Each of these centers identified as a scattering primitive. This is achieved by matching each scattering center from the target image set to a simulated image of a primitive. By utilizing multiple target images (ranging in frequency, orientation, and polarization) a prediction of primitive characteristics is achieved. In principle with sufficient angular realizations the scattering primitives can be placed in three-dimensional space. Leveraging synergistic machine/deep learning target recognition techniques under development, including their real and synthetic training data, a Doppler-polarimetric-based NCTR technique could be a powerful enhancement to other complementary NCTR techniques.

Work produced in Phase II may become classified. Note: The prospective contractor(s) must be U.S. owned and operated with no foreign influence as defined by DoD 5220.22-M, National Industrial Security Program Operating Manual, unless acceptable mitigating procedures can and have been implemented and approved by the Defense Counterintelligence and Security Agency (DCSA) formerly Defense Security Service (DSS). The selected contractor must be able to acquire and maintain a secret level facility and Personnel Security Clearances. This will allow contractor personnel to perform on advanced phases of this project as set forth by DCSA and NAVAIR in order to gain access to classified information pertaining to the national defense of the United States and its allies; this will be an inherent requirement. The selected company will be required to safeguard classified material IAW DoD 5220.22-M during the advanced phases of this contract.

PHASE I: Utilizing computational electromagnetics modeling applications, generate aspect-dependent polarimetric scattering matrices of multiple aircraft, and investigate the use of Doppler polarimetric decomposition as an NCTR technique. Assess whether this information provides a robust discriminate between similar aircraft types. Consider the impact of model fidelity in the stability of Doppler polarimetric features. Assess the relative enhancement of NCTR performance when used in conjunction with legacy techniques. The Phase I effort will include prototype plans to be developed under Phase II.

PHASE II: Develop and demonstrate a Doppler polarimetric NCTR exploitation signal processing approach using collected field data supplied by the Navy sponsor. Assess the performance as a function of dwell time and illumination geometry. Develop mode design and tactical utilization recommendations for radar systems identified by the Navy sponsor.

Work in Phase II may become classified. Please see note in Description paragraph.

PHASE III DUAL USE APPLICATIONS: Complete development, perform final testing, integrate, and transition the final solution to naval airborne NCTR system. Doppler polarimetric radar techniques have the potential to provide additional insights into remote sensing of weather and other environmental effects.


1.       Xu, F., Wang, H., Jin, Y. Q., Liu, X., Wang, R., & Deng, Y. (2015). Impact of cross-polarization isolation on polarimetric target decomposition and target detection. Radio Science, 50(4), 327-338. https://ieeexplore.ieee.org/document/7771908

2.       Chamberlain, N. E., Walton, E. K., & Garber, F. D. (1991). Radar target identification of aircraft using polarization-diverse features. IEEE Transactions on Aerospace and Electronic Systems, 27(1), 58-67. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=68148

3.       Cameron, W. L., & Leung, L. K. (1990, May). Feature motivated polarization scattering matrix decomposition. In IEEE International Conference on Radar (pp. 549-557). IEEE. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=201088

4.       Andre, D. B., & Tham, C. L. (2002, October). Target decomposition through polarimetric, disjoint Doppler and frequency band (I) SAR images. In RADAR 2002 (pp. 531-535). IET. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1174775

5.       Martorella, M., Berizzi, F., Soleti, R., Cantini, L., Corucci, A., Haywood, B., & Palmer, J. (2006, July). Target classification by means of fully polarimetric ISAR images. In 2006 IEEE International Symposium on Geoscience and Remote Sensing (pp. 141-144). IEEE. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4241188

6.       Yanovsky, F. J. (2009, November). Doppler-polarimetric radar system for recognition of distributed objects. In 2009 IEEE International Conference on Microwaves, Communications, Antennas and Electronics Systems (pp. 1-4). IEEE. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5386046

7.       Keydel, W. (2007). Polarimetry and Interferometry Applications. German Aerospace Research Center (DLR), Wessling (Germany) Microwaves and Radar Inst. https://www.sto.nato.int/publications/STO%20Educational%20Notes/RTO-EN-SET-081bis/EN-SET-081bis-11.pdf


KEYWORDS: Polarimetric; Radar; Non-Cooperative Target Recognition; Electromagnetic Scattering; Inverse Synthetic Aperture Radar; Doppler


The Navy Topic above is an "unofficial" copy from the Navy Topics in the DoD 23.1 SBIR BAA. Please see the official DoD Topic website at www.defensesbirsttr.mil/SBIR-STTR/Opportunities/#announcements for any updates.

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Topic Q & A

2/21/23  Q. Are you primarily interested in horizontal and vertical polarization?
   A. Consider two cases: 1. Fully polarimetric with good axial ratio and x-pol isolation over scan. 2. Slant left and slant right also well behaved over scan.
2/13/23  Q. 1) Which aircraft, specifically, are of interest?
2) Are CAD models of the targets of interest provided?
3) What algorithms have been used in the past to discriminate the targets given the proposed data structures? (Deep learning, template matching?)
4) Are sensor specs or a sensor model provided?
5) What are typical operating conditions of interest?
6) Is measured data for use to calibrate our synthetic data available?
7) Can you expand on the definition of "non-stable" target?
8) Can you clarify the structure of an aspect-dependent polarimetric scattering matrix?
9) With respect to model fidelity - does this include material properties?
   A. 1) For the purposes of synthetic data generation via CEM modeling we suggest for phase I focus on wavelengths from UHF through S band. Candidate aircraft to model which have similar dimensions could be a Boeing 737-800 and a Xian H-6/
2) You need to either purchase or build the models. Fidelity at the wavelengths of interest should be an consideration.
3) We can�t comment on other techniques here, but if you propose a training intensive approach you need to justify how it will be adequately trained.
4) You can assume excellent axial ratio and x-pol isolation (25 dB) over scan. Some system may only provide slant left and right. That is sufficient information for you to execute phase I.
5) The host radar is on a Navy aircraft. Altitude is variable. Precipitation is 0-4 mm/hr.
6) No, not in Phase I. The synthetic data used in phase I is for a feasibility analysis only. If you approach relies on synthetic data for training you need to demonstrate that it is adequate for training.
7) Moving with moving parts (rotors, turbine sections, �)
8) The target is under track so you know the aspect and the scattering is aspect dependent.
9) Consider model fidelity relative to wavelength. Assume PEC structures in Phase I.

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