Surf zone measurements and airborne imagery were collected off North Carolina. The data and a surf zone index based
on the properties of water clarity, waves, and foam were used to predict imager performance for objects of varying
reflectivity and contrast.
The Airborne Littoral Reconnaissance Technology (ALRT) program has successfully demonstrated the Wide-Field Airborne Laser Diode Array Illuminator (ALDAI-W). This illuminator is designed to illuminate a large area from the air with limited power, weight, and volume. A detection system, of which the ALDAI-W is a central portion, is capable of detecting surface-laid minefields in absolute darkness, extending the allowed mission times to night operations. This will be an overview report, giving processing results and suggested paths for additional development.
The Airborne Littoral Reconnaissance Technologies (ALRT) project has developed and successfully demonstrated a nighttime operational minefield detection capability using commercial off-the-shelf high-power Laser Diode Arrays (LDAs). The Coastal System Station's ALRT project, under funding from the Office of Naval Research (ONR), has been designing, developing, integrating, and testing commercial arrays using a Cessna airborne platform over the last several years. This has led to the development of three test bed variants, as reported on last year: the Airborne Laser Diode Array Illuminator prototype (ALDAI-P), the original commercial array version (ALDAI-C), and the most recent wide field-of-view commercial version (ALDAI-W). Using the ALDAI-W variant because of its increased operational capabilities with higher altitudes and wider field of views, ALRT recently demonstrated nighttime operation by detecting minefields over several background variations, expanding Naval reconnaissance capabilities that had been previously limited to daytime operation. This paper describes the demonstration and shows results of the ALDAI-W test.
The Airborne Littoral Reconnaissance Technologies (ALRT) project has developed and tested a nighttime operational minefield detection capability using commercial off-the-shelf high-power Laser Diode Arrays (LDAs). The Coastal System Station’s ALRT project, under funding from the Office of Naval Research (ONR), has been designing, developing, integrating, and testing commercial arrays using a Cessna airborne platform over the last several years. This has led to the development of the Airborne Laser Diode Array Illuminator wide field-of-view (ALDAI-W) imaging test bed system. The ALRT project tested ALDAI-W at the Army’s Night Vision Lab’s Airborne Mine Detection Arid Test. By participating in Night Vision’s test, ALRT was able to collect initial prototype nighttime operational data using ALDAI-W, showing impressive results and pioneering the way for final test bed demonstration conducted in September 2003. This paper describes the ALDAI-W Arid Test and results, along with processing steps used to generate imagery.
The objective of the Office of Naval Research (ONR) Rapid Overt Reconnaissance (ROR) program and the Airborne Littoral Reconnaissance Technologies (ALRT) project's LAMBS effort is to determine if electro-optical spectral discriminants exist that are useful for the detection of land mines in littoral regions. Statistically significant buried mine overburden and background signature data were collected over a wide spectral range (0.35 to 14 µm) to identify robust spectral features that might serve as discriminants for new airborne sensor concepts. LAMBS has expanded previously collected databases to littoral areas - primarily dry and wet sandy soils - where tidal, surf, and wind conditions can severely modify spectral signatures. At AeroSense 2003, we reported completion of three buried mine collections at an inland bay, Atlantic and Gulf of Mexico beach sites. We now report LAMBS spectral database analyses results using metrics which characterize the detection performance of general types of spectral detection algorithms. These metrics include mean contrast, spectral signal-to-clutter, covariance, information content, and spectral matched filter analyses. Detection performance of the buried land mines was analyzed with regard to burial age, background type, and environmental conditions. These analyses considered features observed due to particle size differences, surface roughness, surface moisture, and compositional differences.
The surf zone is a challenging environment for conducting mine countermeasures operations. The performance of acoustic sensors in this environment is extremely limited. Airborne LIDAR sensors have significantly better prospects for successfully working in this environment. However, the complex environment will be a driving factor limiting their performance. The environmental factors influencing the performance of airborne LIDAR sensors will be examined in this paper. These factors can be highly dynamic. Breaking surf action causes bottom sediment resuspension and the formation of bubbles and foam. The resuspended sediments then begin the process of settling, while the bubbles and foam begin to dissipate. All of these phenomena impact the optical properties of the water, which, in turn, impact the performance of the LIDAR system. An experiment was designed and conducted to study the impact of these dynamic processes on the optical properties of the water. The experiment was conducted in September 2002 at the Army Corp of Engineers Field Research Facility in Duck, North Carolina. Preliminary results from the analysis of this data are presented here. This work is being conducted by the Airborne Littoral Reconnaissance Technology (ALRT) project under ONR sponsorship.
KEYWORDS: Sensors, Cameras, Receivers, Imaging systems, LIDAR, Stereoscopic cameras, Land mines, Signal to noise ratio, 3D image processing, Image processing
Under the Office of Naval Research's Organic Mine Countermeasures Future Naval Capabilities (OMCM FNC) program, Lite Cycles, Inc. is developing an innovative and highly compact airborne active sensor system for mine and obstacle detection in very shallow water (VSW), through the surf-zone (SZ) and onto the beach. The system uses an
innovative LCI proprietary integrated scanner, detector, and telescope (ISDT) receiver architecture. The ISD tightly couples all receiver components and LIDAR electronics to achieve the system compaction required for tactical UAVintegration while providing a large aperture. It also includes an advanced compact multifunction laser transmitter; an industry-first high-resolution, compact 3-D camera, a scanning function for wide area search, and temporally
displaced multiple looks on the fly over the ocean surface for clutter reduction. Additionally, the laser will provide time-multiplexed multi-color output to perform day/night multispectral imaging for beach surveillance. New processing algorithms for mine detection in the very challenging surf-zone clutter environment are under development, which offer the potential for significant processing gains in comparison to the legacy approaches. This paper reviews the legacy system approaches, describes the mission challenges, and provides an overview of the ROAR system architecture.
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.
The objective of the Office of Naval Research (ONR) Rapid Overt Reconnaissance (ROR) program and the Airborne Littoral Reconnaissance Technologies project's Littoral Assessment of Mine Burial Signatures (LAMBS) contract is to determine if electro-optical spectral discriminants exist that are useful for the detection of land mines located in littoral regions. Statistically significant buried mine overburden and background signature data were collected over a wide spectral range (0.35 to 14 μm) to identify robust spectral features that might serve as discriminants for new airborne sensor concepts. The LAMBS program further expands the hyperspectral database previously collected and analyzed on the U.S. Army's Hyperspectral Mine Detection Phenomenology program [see "Detection of Land Mines with Hyperspectral Data," and "Hyperspectral Mine Detection Phenomenology Program," Proc. SPIE Vol. 3710, pp 917-928 and 819-829, AeroSense April 1999] to littoral areas where tidal, surf, and wind action can additionally modify spectral signatures. This work summarizes the LAMBS buried mine collections conducted at three beach sites - an inland bay beach site (Eglin AFB, FL, Site A-22), an Atlantic beach site (Duck, NC), and a Gulf beach site (Eglin AFB, FL, Site A-15). Characteristics of the spectral signatures of the various dry and damp beach sands are presented. These are then compared to buried land mine signatures observed for the tested background types, burial ages, and environmental conditions experienced.
The Airborne Littoral Reconnaissance Technologies (ALRT) Project has demonstrated a nighttime operational minefield detection capability using commercial off-the-shelf high-power Laser Diode Arrays (LDAs). Historically, optical aerial detection of minefields has primarily been limited to daytime operations but LDAs promise compact and efficient lighting to allow for enhanced reconnaissance operations for future mine detection systems. When combined with high-resolution intensified imaging systems, LDAs can illuminate otherwise unseen areas. Future wavelength options will open the way for active multispectral imaging with LDAs. The Coastal Systems Station working for the Office of Naval Research on the ALRT project has designed, developed, integrated, and tested both prototype and commercial arrays from a Cessna airborne platform. Detailed test results show the ability to detect several targets of interest in a variety of background conditions. Initial testing of the prototype arrays, reported on last year, was completed and further investigations of the commercial versions were performed. Polarization-state detection studies were performed, and advantageous properties of the source-target-sensor geometry noted. Current project plans are to expand the field-of-view coverage for Naval exercises in the summer of 2003. This paper describes the test collection, data library products, array information, on-going test analysis results, and future planned testing of the LDAs.
JMDT is a Navy/Marine Corps 6.2 Exploratory Development program that is closely coordinated with the 6.4 COBRA acquisition program. The objective of the program is to develop innovative science and technology to enhance future mine detection capabilities. The objective of the program is to develop innovative science and technology to enhance future mine detection capabilities. Prior to transition to acquisition, the COBRA ATD was extremely successful in demonstrating a passive airborne multispectral video sensor system operating in the tactical Pioneer unmanned aerial vehicle (UAV), combined with an integrated ground station subsystem to detect and locate minefields from surf zone to inland areas. JMDT is investigating advanced technology solutions for future enhancements in mine field detection capability beyond the current COBRA ATD demonstrated capabilities. JMDT has recently been delivered next- generation, innovative hardware which was specified by the Coastal System Station and developed under contract. This hardware includes an agile-tuning multispectral, polarimetric, digital video camera and advanced multi wavelength laser illumination technologies to extend the same sorts of multispectral detections from a UAV into the night and over shallow water and other difficult littoral regions. One of these illumination devices is an ultra- compact, highly-efficient near-IR laser diode array. The other is a multi-wavelength range-gateable laser. Additionally, in conjunction with this new technology, algorithm enhancements are being developed in JMDT for future naval capabilities which will outperform the already impressive record of automatic detection of minefields demonstrated by the COBAR ATD.
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: 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.
KEYWORDS: Unmanned aerial vehicles, Video, Video surveillance, Land mines, Global Positioning System, Surveillance, Multispectral imaging, Target detection, Reconnaissance, Reconnaissance systems
The Coastal Battlefield Reconnaissance and Analysis)COBRA) system described here was a Marine Corps Advanced Technology Demonstration (ATD) development consisting of an unmanned aerial vehicle (UAV) airborne multispectral video sensor system and ground station which processes the multispectral video data to automatically detect minefields along the flight path. After successful completion of the ATD, the residual COBRA ATD system participated in the Joint Countermine (JCM) Advanced Concept Technology Demonstration (ACTD) Demo I held at Camp Lejeune, North Carolina in conjunction with JTFX97 and Demo II held in Stephenville, Newfoundland in conjunction with MARCOT98. These exercises demonstrated the COBRA ATD system in an operational environment, detecting minefields that included several different mine types in widely varying backgrounds. The COBRA system performed superbly during these demonstrations, detecting mines under water, in the surf zone, on the beach, and inland, and has transitioned to an acquisition program. This paper describes the COBRA operation and performance results for these demonstrations, which represent the first demonstrated capability for remote tactical minefield detection from a UAV. The successful COBRA technologies and techniques demonstrated for tactical UAV minefield detection in the Joint Countermine Advanced Concept Technology Demonstrations have formed the technical foundation for future developments in Marine Corps, Navy, and Army tactical remote airborne mine detection systems.
Coastal Systems Station under the sponsorship of the Marine Corps Amphibious Warfare Technology Directorate are exploring the use of a Passive Millimeter Wave (PMMW) sensor for stand off airborne mine detection. In the development of any new technology application, there exist a critical need to develop a balanced modeling and measurement capability. Both will complement one another. Nichols Research has established a physics-based image modeling capability for Passive Millimeter Wave (PMMW) systems. This modeling capability has been used to estimate the performance of a PMMW mine detection system. But, in order to accurately predict the performance of a PMMW imaging system, the background clutter characteristics must be characterized and the modeling results verified against measured data. In fact, in the case of a well designed sensor, the background clutter will define the systems overall performance making accurate knowledge of the clutter statistical variations critical. However currently, there is a lack of high resolution PMMW imagery of backgrounds, due to a lack of data collection instrumentation. This paper will present the results from a preliminary PMMW data collection to provide data for the assessment of a PMMW mine detection system. The data collection results will characterize both surface and buried mine detection capabilities under a variety of conditions. It is a well-established fact that no single sensor will be capable of solving the mine detection problem. Instead, a suite of complementary sensors is required. There is however a lack of an extensive data set of sensor modalities collected in a single sample area. Therefore as a secondary objective of this data collection, several sensor modalities will be used to simultaneously collect mine and minefield data. These results will also be presented.
Several mine detection systems are currently under development which will provide airborne mine detection capabilities. For example, the COBRA program utilizes a multispectral video camera which will provide an interim, clear weather, daylight capability when deployed in a Pioneer unmanned aerial vehicle (UAV). The ASTAMIDS program has a dual approach, one contains an active polarized source as well as a passive IR camera and the other only a passive IR imager. Either ASTAMIDS system will provide day/night and limited visibility operation. The use of a PMMW imaging sensor promises to provide day/night and al weather mine detection performance. Attenuation in the MMW regime is not dramatically effected by adverse weather. In addition there is a large contrast between metal targets and the background for air to ground scenarios. Furthermore, due to the long wavelengths, vegetation and soils are not completely opaque in the MMW regime, offering the possibility to detect buried targets under specific conditions. This paper will describe the assessment of an imaging PMMW sensor for mine detection. The results of data collection and modeling analysis will be presented as evidence to the utility and capabilities of the technology to perform under adverse weather conditions.
The Coastal Systems Station of the Naval Surface Warfare Center-Dahlgren Division is developing the Coastal Battlefield Reconnaissance and Analysis (COBRA) system. COBRA is a U.S. Marine Corps advanced technology demonstration utilizing multispectral video sensors deployed in an unmanned aerial vehicle (UAV) for automated minefield detection and location from the surfzone to inland areas. The system automatically encodes the craft attitude and position into video down-link for real-time ground tracking on a map, satellite image, or aerial photograph. The system has been developed and deployed extensively in a Cessna 172 during developmental testing (DT-0) and will undergo preliminary operation testing (OT-0) in a Pioneer UAV in the summer of 1996. This paper reviews the status and results of the COBRA developmental and operational testing.
The Navy's Coastal System Station (CSS) at Panama City, Florida has been investigating the use of multispectral, intensified cameras for standoff minefield detection. In support of CSS, Nichols Research Corporation's Shalimar Florida Office has developed a 'minefield image synethesis tool', (MIST), which is capable of simulating UV to near-IR images of minefields. The MIST software is divided into two major modules, an image generator and an intensified camera model. The image generator (IG) software performs 3D graphics rendering of objects in the scene to produce 2D images as an imaging sensor would see them. The IG models diffuse reflection from sunshine, skyshine, and earthshine. Path transmittances and radiances are accounted for. The sensor spectral band is a user input. Other quantities including reflectances and illumination sources are imput spectrally, making it possible to generate images for different spectral bands, such as those being investigated by CSS. Sensor effects including intensifier/detector response, noise, and analog-to-digital conversion are modeled in the intensified camera model (ICM) software. This paper describes the MIST software and tests that have been performed to validate the software.
The need is described for a system-level integrated treatment of compression and detection methods and several issues are raised. Compression detection examples are provided as a first step in this direction and to illustrate the concepts.
The Coastal Battlefield Reconnaissance and Analysis (COBRA) program is a US Marine Corps Advanced Technology Demonstration (ATD). The objective is to design, develop, and demonstrate an unmanned aerial vehicle (UAV) based passive multispectral video sensor subsystem, to detect and locate obstacles and minefields before and during an amphibious assault, and land combat operations in littoral areas. The COBRA ATD system consisting of an airborne sensor subsystem and a ground station subsystem is described along with the testing program.
The utility and robustness of wavelet features is demonstrated through three practical case studies of detecting objects in multispectral electro-optical imagery, sidescan sonar imagery, and acoustic backscatter. Attention is given to choosing proper waveforms for particular applications. Using artificial neural networks (ANNs), evidence is fused from multiple-waveform types that detect local features. The wavelet waveforms and their dilation and shift parameters are adaptively computed with ANNs to maximize classification accuracy. Emphasis is placed on the acoustic backscatter case study, involving detecting a metallic man-made object from natural and synthetic specular clutter with reverberation noise. The synthetic clutter is shown to be a good model for the natural clutter, which appears promising for avoiding huge data collection efforts for natural clutter and for better delineating the classification boundary. The classifier computes the locations, sizes, and weights of Gaussian patches in time-scale space that contain the most discriminatory information. This new approach is shown to give higher classification rates than an ANN with commonly used power spectral features. The new approach also reduces the number of free parameters in the classifier based on all wavelet features, which leads to simpler implementation for applications and to potentially better generalization to test data.
Wavelet processing followed by a neural network classifier is shown to give higher blob detection rate and lower false alarm rate than simply classifying single pixels by their spectral characteristics. An on-center, off-surround wavelet is shown to be highly effective in removing constant-mean background areas, as well as ramping intensity variations that can occur due to camera nonuniformities or illumination differences. Only a single wavelet dilation is tested in a case study, but it is argued that wavelets at different scales will play a useful role in general. Adaptive wavelet techniques are discussed for registration and sensor fusion.
The design and preliminary evaluation of a low cost video based multispectral camera system developed in the Standoff Mine Detection Ground (SMDG) exploratory development project for the US Marine Corps is described. The system is composed of an intensified and gated video camera fitted with six user selectable filters in a synchronous spinning filter wheel which provides a different spectral filter for each video frame. The camera system is microprocessor controlled for automatic exposure and matched with custom designed spectrally corrected optics. The associated analog and digital image storage and processing equipment and techniques are also discussed. This system has been field tested to detect land mines at long standoff distances.
Range-gated underwater imaging has long been a popular topic for discussion and analysis; however, current laboratory
data from such a system in operation is not readily available. In the summer of 1989, the authors configured a range-gated,
laser-illuminatedimaging system utilizing "off-the-shelf" equipmenttodemonstrate the feasibility ofusing this technique. Using
an intensified, gated, solid state video camera, a pulsed-frequency doubled Nd:YAG laser, a time delay generator, and an optical
delay to compensate for system delays, a range-gated testhed was configured with the capability to record image slices through
the water volume movable in 1 ns increments. A time delay generator and an optical delay to compensate for fixed system
delays were configured to gate the camera to take image slices through the water, moving in 1 ns steps. The tests consisted of
placing the target at various depths to 40 feet as the turbidity of the water was varied and measured. The images were collected
on video tape and later digitized and analyzed. Details of the experimental configuration and test results are presented.
KEYWORDS: Reflectivity, Statistical analysis, In situ metrology, Data acquisition, Ocean optics, Analytical research, Geodesy, Data analysis, Data modeling, Electromagnetism
Estimators of in situ bottom reflectivity are compared to
laboratory measured spectra for several sites. Statistics are
compiled on the relative fits of the estimators spectral curves to
the laboratory data. A best estimator is selected based on these
statistics. It is conjectured that the best in situ estimator
remains the best estimate of the bottom reflectivity even in more
complex ocean bottom areas where laboratory data may no longer
provide accurate values for in situ sediment reflectivity.
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