For over 30 years, the U.S. Army Aviation and Missile Research, Development, and Engineering Center (AMRDEC) has specialized in characterizing the performance of infrared (IR) imaging systems in the laboratory and field. In the late 90’s, AMRDEC developed the Automated IR Sensor Test Facility (AISTF) which allowed efficient deployment testing of aviation and missile IR sensor systems. More recently, AMRDEC has tested many uncooled infrared (UCIR) sensor systems that have size, weight, power, and cost (SWAPC) benefits for certain fielded U.S. Army imaging systems. To compensate for relatively poor detector sensitivities, most UCIR systems operate with very fast focal ratio or F-number (f/#) optics. AMRDEC has recently found that measuring the Noise Equivalent Temperature Difference (NETD) with traditional techniques used with cooled infrared systems produce biased results when applied to systems with faster f/# values or obscurations. Additionally, in order to compare these camera cores or sensor systems to one another, it is imperative to scale the NETD values for f/#, focus distance, and waveband differences accurately. This paper will outline proper measurement techniques to report UCIR camera core and system-level NETD, as well as demonstrate methods to scale the metric for these differences.
Computational imaging techniques can be used to extend the depth of field of imaging sensors such that the sensors become less expensive to build and athermalize with no loss to performance. Optical phase can be manipulated to create an image that is optimized for a detection and tracking algorithm as well as reconstructed digitally to form an image suitable for viewing. A typical low-cost sensor which is used for target detection and tracking may run an algorithm which requires different features and resolution from its imagery than would a system optimized for a human. This offers a unique opportunity to optimize both optics and image processing for a system which can maximize mission performance as well as minimize production cost. Simple computational techniques have not yet been successful in passive, low-signal environments due to noise issues. This study examines the use of a simple computational technique in an algorithmic application in which optimal reconstruction may occur with lower noise. This paper will describe the model, simulation, and prototype which resulted from a detailed and novel system design and modeling process. The goal of this effort is to accurately model the anticipated performance and to prove actual cost savings of a tracking sensor which employs computational imaging techniques.
For over 30 years, the U.S. Army Aviation and Missile Research, Development, and Engineering Center (AMRDEC) has specialized in characterizing the performance of infrared (IR) imaging systems in the laboratory and field. In the late 90’s, AMRDEC developed the Automated IR Sensor Test Facility (AISTF) which allowed efficient deployment testing of Unmanned Aerial Systems (UAS) payloads. More recently, ImageJ has been used predominately as the image processing environment of choice for analysis of laboratory, field, and simulated data. The strengths of ImageJ are that it is maintained by the U.S. National Institute of Health, it exists in the public domain, and it functions on all major operating systems. Three new tools or “plugins” have been developed at AMRDEC to enhance the accuracy and efficiency of analysis. First, a Noise Equivalent Temperature Difference (NETD) plugin was written to process Signal Transfer Function (SiTF) and 3D noise data. Another plugin was produced that measures the Modulation Transfer Function (MTF) given either an edge or slit target. Lastly, a plugin was developed to measure Focal Plane Array (FPA) defects, classify and bin the customizable defects, and report statistics. This paper will document the capabilities and practical applications of these tools as well as profile their advantages over previous methods of analysis.
Infrared imaging is commonly used for performing thermography based on field calibration that simply relates image levels to apparent temperature levels using field blackbodies. Under normal conditions, the correlation between the image levels and blackbody temperature is strong, allowing conversion of the raw data into units of blackbody-equivalent temperature without consideration of other factors. However, if certain instrument anomalies are present, a compensation procedure that involves more in-depth sensor characterization may be required. The procedure, which uses an analysis of temperature-dependent dark current, optical emissions, and detector response, is described along with results for a specific case. The procedure involves first cold soaking a thermal camera and then observing the cooldown behavior of the sensor under non-stressing conditions. Variations in environmental temperature levels are then used to observe cooler performance and dark current levels. A multi-variate linear regression is performed that allows temperature-dependent dark current, lens emission, lens transmission, and detector quantum efficiency to be fully characterized. The resulting data describe for each image pixel a relationship between the scene temperature and the observed values of image signal, detector temperature, and camera temperature. The procedure has been applied successfully to a thermal imager used to collect field data while suffering from instrument anomalies due to a faulty cooler. Using the resulting characterization data for the pixel-dependent dark current, image data collected with the thermal imager was compensated. The compensation involved using spatial filtering to determine temperature shifts caused by the faulty cooler based on the predictable pattern of pixel-to-pixel variations in dark current. The estimated temperature shift was used to compute a compensation offset for each pixel based on its known dark current coefficient. The compensated image data, while still degraded, was sufficiently corrected for the predictable effects of dark current variations to allow valid thermography to be performed.
The US Army Aviation and Missile Research, Development, and Engineering Center (AMRDEC)
is developing a micro-uncooled infrared (IR) capability for small unmanned aerial systems
(SUAS). In 2007, AMRDEC procured several uncooled microbolometers for lab and field test
evaluations, and static tower tests involving specific target sets confirmed initial modeling and
simulation predictions. With these promising results, AMRDEC procured two captive flight test
(CFT) vehicles and, in 2008, completed numerous captive flights to capture imagery with the
micro-uncooled infrared sensors. Several test configurations were used to build a comprehensive
data set. These configurations included variations in look-down angles, fields of view (FOV),
environments, altitudes, and target scenarios. Data collected during these field tests is also being
used to develop human tracking algorithms and image stabilization software by other AMRDEC
personnel. Details of these ongoing efforts will be presented in this paper and will include: 1)
onboard digital data recording capabilities; 2) analog data links for visual verification of imagery;
3) sensor packaging and design; which include both infrared and visible cameras; 4) field test and
data collection results; 5) future plans; 6) potential applications. Finally, AMRDEC has recently
acquired a 17 μm pitch detector array. The paper will include plans to test both 17 μm and 25 μm
microbolometer technologies simultaneously in a side-by-side captive flight comparison.