We report the development and initial testing of the Lockheed Martin first-article, single-stage, compact, coaxial, Fast Cooldown Pulse Tube Microcryocooler (FC-PTM). The new cryocooler supports cooling requirements for emerging large, high operating temperature (105-150K) infrared focal plane array sensors with nominal cooling loads of ~300 mW @105K @293K ambient. This is a sequel development that builds on our inline and coaxial pulse tube microcryocoolers reported at CEC 20137, ICC188,9, and CEC201510. The new FC-PTM and the prior units all share our long life space technology attributes, which typically have 10 year life requirements1. The new prototype microcryocooler builds on the previous development by incorporating cold head design improvements in two key areas: 1) reduced cool-down time and 2) novel repackaging that greatly reduces envelope. The new coldhead and Dewar were significantly redesigned from the earlier versions in order to achieve a cooldown time of 2-3 minutes-- a projected requirement for tactical applications. A design approach was devised to reduce the cold head length from 115mm to 55mm, while at the same time reducing cooldown time. We present new FC-PTM performance test measurements with comparisons to our previous pulse-tube microcryocooler measurements and design predictions. The FC-PTM exhibits attractive small size, volume, weight, power and cost (SWaP-C) features with sufficient cooling capacity over required ambient conditions that apply to an increasing variety of space and tactical applications.
We report the development and initial testing of the Lockheed Martin first-article, single-stage, compact, coaxial, Fast Cooldown Pulse Tube Microcryocooler (FC-PTM). The new cryocooler supports cooling requirements for emerging large, high operating temperature (105-150K) infrared focal plane array sensors with nominal cooling loads of ~300 mW @105K @293K ambient. This is a sequel development that builds on our inline and coaxial pulse tube microcryocoolers reported at CEC 20137, ICC188,9, and CEC201510. The new FC-PTM and the prior units all share our long life space technology attributes, which typically have 10 year life requirements1. The new prototype microcryocooler builds on the previous development by incorporating cold head design improvements in two key areas: 1) reduced cool-down time and 2) novel repackaging that greatly reduces envelope. The new coldhead and Dewar were significantly redesigned from the earlier versions in order to achieve a cooldown time of 2-3 minutes-- a projected requirement for tactical applications. A design approach was devised to reduce the cold head length from 115mm to 55mm, while at the same time reducing cooldown time. We present new FC-PTM performance test measurements with comparisons to our previous pulse-tube microcryocooler measurements and design predictions. The FC-PTM exhibits attractive small size, volume, weight, power and cost (SWaP-C) features with sufficient cooling capacity over required ambient conditions that apply to an increasing variety of space and tactical applications.
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 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.
Spatial-spectral anomaly detection (the “RX Algorithm”) has been exploited on the USMC's Coastal Battlefield Reconnaissance and Analysis (COBRA) Advanced Technology Demonstration (ATD) and several associated technology base studies, and has been found to be a useful method for the automated detection of surface-emplaced antitank land mines in airborne multispectral imagery. RX is a complex image processing algorithm that involves the direct spatial convolution of a target/background mask template over each multispectral image, coupled with a spatially variant background spectral covariance matrix estimation and inversion. The RX throughput on the ATD was about 38X real time using a single Sun UltraSparc system. A goal to demonstrate RX in real-time was begun in FY01. We now report the development and demonstration of a Field Programmable Gate Array (FPGA) solution that achieves a real-time implementation of the RX algorithm at video rates using COBRA ATD data. The approach uses an Annapolis Microsystems Firebird PMC card containing a Xilinx XCV2000E FPGA with over 2,500,000 logic gates and 18MBytes of memory. A prototype system was configured using a Tek Microsystems VME board with dual-PowerPC G4 processors and two PMC slots. The RX algorithm was translated from its C programming implementation into the VHDL language and synthesized into gates that were loaded into the FPGA. The VHDL/synthesizer approach allows key RX parameters to be quickly changed and a new implementation automatically generated. Reprogramming the FPGA is done rapidly and in-circuit. Implementation of the RX algorithm in a single FPGA is a major first step toward achieving real-time land mine detection.
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.
The Coastal Systems Station, in concert with Xybion Corp. has developed a tunable-filter multispectral imaging sensor for use in airborne reconnaissance. The sensor was completed in late 1999, and laboratory characterization and field- testing has been conducted since. The Tunable Filter Multispectral Camera (TFMC) is an intensified, gated, and tunable multispectral imaging camera that provides three simultaneous channels of 10-bit digital and 8-bit analog video from the near-UV to the near-IR. Exposure and gain can be automatically or manually controlled for each channel, and response has been linearized for approximate radiometric use. Additionally, each of the three channels as a separate programmable liquid-crystal tunable filter with a selectable center wavelength settings to which can be applied 100 different retardances for each of three channels. This paper will present setups, analysis methods, and preliminary results for both the laboratory characterization and field- testing of the TFMC. Laboratory objectives include measures of sensitivity, noise, and linearity. Field testing objectives include obtaining the camera response as the lighting conditions approached sunset of a clear day, signal-to-clutter ratios for a multiplicity of channel wavelength combinations and polarizations against several backgrounds, and resolution performance in field-conditions.
Statistically significant sets of buried anti-tank mine and background electro-optic spectral signatures were collected and analyzed by the Veridian ERIM International team under the US Army's Night Vision and Electronic Sensors Directorate Hyperspectral Mine Detection Phenomenology FY98/99 Program as reported last year. Those analyses established predicted buried mine spectral discrimination performance in key practical sensor spectral regions using typical multispectral sensor bandwidths spanning 20 to 200 nm. This year, we report further analyses of selected sets of HMDP data that quantitatively predict performance for two specific cases of joint spectral regions. This work exhibits these initial results and compares the predicted buried mine spectral discrimination performance determined from the joint and the single spectral regions.
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.
Alexandra Smith, Arthur Kenton, Robert Horvath, Linnea Nooden, Jennifer Michael, James Wright, J. Mars, James Crowley, Marc Sviland, Stan Causey, David Lee, Mary Williams, Kurt Montavon
The objective of the US Army Hyperspectral Mine Detection Phenomenology program was to determine if spectral disciminants exist that are useful for the detection of land mines. A primary goal wa to determine the presence and persistence of spectral features produced by buried anti- tank mines as associated with soil properties and vegetation changes over time. Details of the collections are documented in the ERIM International Technical Report 10012200-15-T, 'Mine Spectral Signature Collections and Data Archive', March 1999. This paper describes the HMDP project and focuses on the initial phase of controlled experimental measurements of spectral mine signatures in ground-based US collections. The foreign data collections are not addressed in this paper. Some of the HMDP project's mine spectral signature result are highlighted here. Detailed analyses of these data were performed and is described in a companion paper in this conference titled 'Detection of Land Mines with Hyperspectral Data'.
The objective of the US Army Hyperspectral Mine Detection Phenomenology program was to determine if spectral discriminants exist that are useful for the detection of land mines. Statistically significant mine signature data were collected over a wide spectral range and analyzed to identify robust spectral features that might serve as discriminants for new airborne sensor concepts. Detection metrics which characterize the deductibility of land miens and which predict the detection performance of a general class of hyperspectral detection algorithms were selected and applied. Detection performance of land mines was analyzed against background type, age of buried miens and possible sensor design parameters. This paper describes the result of this analysis and present EO/IR hyperspectral sensor and algorithm design concepts that could potentially be used to operationally detect buried land mines.
A statistical parametric multispectral sensor performance model was developed by ERIM to support mine field detection studies, multispectral sensor design/performance trade-off studies, and target detection algorithm development. A key element in this performance model is the influence of the background on the target's multispectral statistics due to the size and shape of the target under the sensor's point spread function and pixel sampling function. The multispectral statistics of interest include the first-order (mean) and second-order moments (covariance) of the target's radiance signature. This paper presents a formulation which not only considers the effects of a multispectral sensor with a single point spread function, but also considers the joint effects of multiple, potentially misregistered, point spread functions on the target's covariance statistics. The model and an example of sensor point spread function and pixel sampling function effects on the target's spectral statistics are presented.
A statistical parametric multispectral sensor performance model was developed by ERIM to support mine field detection studies, multispectral sensor design/performance trade-off studies, and target detection algorithm development. The overall model incorporates four components; a mission flight model, a multispectral target and background signature model, a multispectral sensor model, and a multispectral target detection model. Emphasis is placed on estimating the effects of mission multispectral target detection algorithms. Thus, the model ideally supports mission and multispectral sensor trade studies which require optimization of the system's overall target detection performance. The model and a typical example of performance prediction results are presented.
A statistical parametric multispectral sensor performance model was developed by ERIM to support mine field detection studies, multispectral sensor design/performance trade-off studies, and target detection algorithm development. The model assumes target detection algorithms and their performance models which are based on data assumed to obey multivariate Gaussian probability distribution functions (PDFs). The applicability of these algorithms and performance models can be generalized to data having non-Gaussian PDFs through the use of transforms which convert non-Gaussian data to Gaussian (or near-Gaussian) data. An example of one such transform is the Box-Cox power law transform. In practice, such a transform can be applied to non-Gaussian data prior to the introduction of a detection algorithm that is formally based on the assumption of multivariate Gaussian data. This paper presents an extension of these techniques to the case where the joint multivariate probability density function of the non-Gaussian input data is known, and where the joint estimate of the multivariate Gaussian statistics, under the Box-Cox transform, is desired. The jointly estimated multivariate Gaussian statistics can then be used to predict the performance of a target detection algorithm which has an associated Gaussian performance model.
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