SBIR-STTR Award

RDRTec has made significant advancements in the automated classification of ships at long ranges using real time feature extraction from Inverse Synthetic Aperture Radar (ISAR) imagery. RDRTecs Maritime Classification Aid (MCA) can classify a ship to its fine naval class level using ISAR data collected at long ranges in all environments. While physical dimensions of major structural elements of the ship provide the primary features that feed the classification expert system, micro-Doppler based signatures associated with rotating antennas have been shown to provide important additional information to support separation between ship classes with similar physical features. The objective of this research project is to expand the exploitation of micro-Doppler signatures further by identifying ship structural vibrations due to the movement of the ship through water and the ships power plant. In addition to improving classification of ship types that are difficult to separate from similar ship types (confusers), we will explore the use of vibration induced micro-Doppler artifacts for battle damage assessment and fingerprinting of a specific hull. RDRTec proposes to attack this topic in collaboration with a diverse, collaborative set of partners in order to examine the problem from multiple points of view, leverage a broad range of expertise, and increase the likelihood of transitioning the results to the US war fighter. This team is comprised of: RDRTec (small business research prime), Duke University (research institute for this topic), and the University of Michigan (additional subcontractor). Dr. Nickolas Vlahopoulos from the University of Michigan will apply the Finite Element Analysis (FEA) methods which he developed for vibro-acoustic analysis of complex Naval systems to generate simulated vibration data sets for ship cases to use in assessing the feasibility of the extraction of micro-Doppler signatures. RDRTec will generate simulated ISAR returns based on the vibration datasets. The resulting radar measurements will be processed at RDRTec using a modified version of algorithms currently used to extract rotator micro-Doppler artifacts and in parallel processed by Dr. Jeffrey Krolik from Duke University using two stage neural network (NN) techniques. This will allow us to examine the feasibility of both physics and NN based approaches for exploiting vibration induced micro-Doppler. Data sets will be generated for ships of the same size and shape but with different vibration characteristics to assess the ability to distinguish ships which are identical in all other regards.

Benefit:
The Phase I feasibility study will: Determine under what conditions are vibration micro-Doppler signatures large enough to be reliably extracted from ISAR measurements. Determine the extent to which vibration micro-Doppler signatures vary enough to be useful in separating ships of similar sizes and shapes. Explore if when available, can interferometric or multi-static techniques be used to improve the identification of vibration micro-Doppler signatures. If the approach is demonstrated to be feasible, RDRTec will refine the algorithms explored in phase I and integrate them into the existing Maritime Classification Aids toolkit which classifies targets to a fine naval class from ISAR imagery. This enhancement will provide an improved probability of correct classification for distinguishing ships of similar sizes and shapes. The value of this work to radar manufacturers is illustrated by Telephonics Letter of Support where they state the approach addresses significant tactical issues and can be readily transitioned to both a broad range of currently fielded radar systems and future systems such as Telephonics AN/ZPY-4, APS-143, AN/APS-147, AN/APS-153, and MOSAIC . RDRTec has a proven record of transitioning SBIR technologies such as MCA through the following three approaches: Licensing of technology to primes for integration with their existing products. Example: Licensing MCA to Telephonics for integration with their maritime surveillance radar systems. Partnering with primes to leverage their manufacturing and integration capability Example: Partnering with Raytheon to bring our ISAR mode to a family of COARPs Radar Systems Working directly with the DoD to integrate our technologies with their mission systems Example: Integrating MCA with Minotaur for transition into Triton, Fire Scout, Poseidon, and MH60-R All of these will be used for transitioning the exploitation of vibration micro-Doppler developed under this SBIR.

Keywords:
Neural networks, Neural networks, Vibration, Maritime Target Classification, ISAR

RDRTec has made significant advancements in the automated classification of ships at long ranges using real time feature extraction from Inverse Synthetic Aperture Radar (ISAR) imagery. RDRTecs Maritime Classification Aid (MCA) can classify a ship to its fine naval class level using ISAR data collected at long ranges in all environments. While physical dimensions of major structural elements of the ship provide the primary features that feed the classification expert system, micro-Doppler based signatures associated with rotating antennas have been shown to provide important additional information to support separation between ship classes with similar physical features. The objective of this project is to improve classification performance through the exploitation of micro-Doppler signatures by (1) identifying ship structural vibrations due to movement of the ship through water and the ships power plant and (2) expanding the exploitation of rotating antenna signatures. In addition to improving classification of ship types that are difficult to separate from similar ship types (confusers), we will explore the use of vibration induced micro-Doppler artifacts for battle damage assessment, feature aided tracking, and fingerprinting of a specific hull. RDRTec proposes to continue to attack this topic in collaboration with a diverse set of partners to examine the problem from multiple viewpoints, leverage a broad range of expertise, and increase the likelihood of transitioning the results to the US war fighter. This team, successfully united in Phase I, is comprised of: RDRTec (small business research prime), Duke University (research institute for this topic), and the University of Michigan (additional subcontractor). Dr. Nickolas Vlahopoulos group at the Univ. of Michigan will apply Finite Element Analysis (FEA) methods which he developed for vibro-acoustic analysis of complex Naval systems to generate simulated vibration data sets for ship cases to use in assessing the feasibility of the extraction of micro-Doppler signatures and training machine learning-based classifiers. RDRTec will generate simulated ISAR returns from those datasets. Resulting radar measurements will be processed at RDRTec using modified versions of physics-based algorithms currently used to extract rotator micro-Doppler artifacts. Using physics-informed data-driven machine learning techniques, Dr. Jeffrey Kroliks group at Duke University will use a multi-time-scale neural network (NN) using micro-Doppler signatures from vibrating and rotating shipboard machinery for vessel classification. This will allow us to examine the feasibility of both physics based expert systems and machine learning approaches for exploiting vibration and rotator induced micro-Doppler.

Benefit:
At the end of this phase II effort, RDRTec will have completed the design and development of new capabilities to extract micro-Doppler features from maritime surface vessels using airborne radar ISAR modes. These capabilities will be integrated into RDRTecs Maritime Classification Aid (MCA) toolkit for improved long range maritime target classification. This enhancement will provide an improved probability of correct classification for distinguishing ships of similar sizes and shapes. The techniques will also be made available as a building block to be used within other tool suites addressing NAVAIR and ONR needs such as damage assessment, target fingerprinting, and feature aided tracking. The value of this work to radar manufacturers is illustrated by Telephonics Letter of Support where they state the approach addresses significant tactical issues and can be readily transitioned to both a broad range of currently fielded radar systems and future systems such as Telephonics AN/ZPY-4, APS-143, AN/APS-147, AN/APS-153, and MOSAIC . RDRTec has a proven record of transitioning SBIR technologies such as MCA through the following three approaches: Licensing of technology to primes for integration with their existing products. Example: Licensing MCA to Telephonics for integration with their maritime surveillance radar systems. Partnering with primes to leverage their manufacturing and integration capability. Example: Partnering with Raytheon to bring our ISAR mode to a family of COARPs Radar Systems Working directly with the DoD to integrate our technologies with their mission systems. Example: Integrating MCA with Minotaur for transition into Triton, Fire Scout, Poseidon, and MH60-R

Keywords:
MCA, Micro-Doppler, ISAR, Target Classification