The Anti-Invasion Mine Signature Measurement and Assessment for Remote Targeting (AIMSMART) program has undertaken a lidar mine signature data collection for ONR to characterize electro-optic (EO) signatures of anti-invasion mines and environmental factors affecting their detection in the littorals. Two lidar sensors, one 3-D and one polarimetric, both developed by Arete, were fielded at the FRF test facility in Duck, NC. Data were collected with these sensors over a wide variety of mine targets, obstacles, backgrounds, water quality, and wave movements. The principle goal of this analysis is to characterize lidar signature features, especially 3-D, of in-water mines and correlate those features to physical processes in the VSW and SZ environments. This paper describes the approach to characterizing these mine signatures and presents initial results from the analysis.
AN initial automated band selection algorithm suitable for real-time application with tunable multispectral cameras is presented for multispectral target detection. The method and algorithm were developed from analyses of several background and target signatures collected from a field test using the prototype Tunable Filter Multispectral Camera (TFMC). Target and background data from TFMC imagery were analyzed to determine the detection performance of 32,768 unique 3-band channel combinations in the visible through and near IR spectral regions. This tuning knowledge base was analyzed to develop rules for an initial dynamic tuning algorithm. The performance data was sorted by conventional means to determine the best 3-band combinations. Methods were then developed to determine performance enhancing band sets for particular backgrounds and a variety of targets. This knowledge is then used in an algorithm to affect a real-time 3-band tuning capability. Additional band sets for real-time background categorization are chosen by both the ability to spectrally detect of one background from another. This work will illustrate an example of the performance results form the analysis for three targets on various backgrounds.
KEYWORDS: Land mines, Performance modeling, Detection and tracking algorithms, Statistical analysis, Sensors, Monte Carlo methods, Coastal modeling, Systems modeling, Analytical research, Multispectral imaging
A statistical performance analysis of the USMC Coastal Battlefield Reconnaissance and Analysis (COBRA) Minefield Detection (MFD) Model has been performed in support of the COBRA ATD Program under execution by the Naval Surface Warfare Center/Dahlgren Division/Coastal Systems Station . This analysis uses the Veridian ERIM International MFD model from the COBRA Sensor Performance Evaluation and Computational Tools for Research Analysis modeling toolbox and a collection of multispectral mine detection algorithm response distributions for mines and minelike clutter objects. These mine detection response distributions were generated form actual COBRA ATD test missions over littoral zone minefields. This analysis serves to validate both the utility and effectiveness of the COBRA MFD Model as a predictive MFD performance too. COBRA ATD minefield detection model algorithm performance results based on a simulate baseline minefield detection scenario are presented, as well as result of a MFD model algorithm parametric sensitivity study.
A new multispectral camera response model has been developed in support of the US Marine Corps (USMC) Coastal Battlefield Reconnaissance and Analysis (COBRA) Advanced Technology Demonstration (ATD) Program. This analytical model accurately estimates response form five Xybion intensified IMC 201 multispectral cameras used for COBRA ATD airborne minefield detection. The camera model design is based on a series of camera response curves which were generated through optical laboratory test performed by the Naval Surface Warfare Center, Dahlgren Division, Coastal Systems Station (CSS). Data fitting techniques were applied to these measured response curves to obtain nonlinear expressions which estimates digitized camera output as a function of irradiance, intensifier gain, and exposure. This COBRA Camera Response Model was proven to be very accurate, stable over a wide range of parameters, analytically invertible, and relatively simple. This practical camera model was subsequently incorporated into the COBRA sensor performance evaluation and computational tools for research analysis modeling toolbox in order to enhance COBRA modeling and simulation capabilities. Details of the camera model design and comparisons of modeled response to measured experimental data are presented.
KEYWORDS: Detection and tracking algorithms, LIDAR, Algorithm development, Target detection, Sensors, 3D acquisition, Signal processing, Image segmentation, 3D modeling, Digital signal processing
Use of ladar seekers for autonomous vehicle identification and targeting from short range, expendable munitions is increasingly of interest due to the inherently high resolution shape data and the relatively low unit cost of the sensor. In addition, low-cost digital signal processors are now available that can manage the computational workload required for autonomous operation in a wide variety of tactical scenarios. A set of detection, segmentation, and vehicle identification algorithms have been developed which have been demonstrated on real and synthetic seeker data and have been targeted for the architecture and resources available on a tactically realistic processor. Results of preliminary algorithm testing are presented.
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