Mass markets, including mobile phones and automotive sensors, drive rapid developments of imaging technologies toward high performance, low cost sensors, even for the thermal infrared. Good infrared calibration blackbody sources have remained relatively costly, however. Here we demonstrate how to make low-cost reference sources, making quantitative infrared radiometry more accessible to a wider community. Our approach uses ordinary construction materials combined with low cost microcontrollers, digital temperature sensors and foil heater elements from massmarket 3D printers. Blackbodies are constructed from a foil heater of some chosen size and shape, attached to the back of a similarly shaped aluminum plate coated with commercial black paint, which normally exhibits high emissivity. The emissivity can be readily checked by using a thermal imager to view the reflection of a hot object. A digital temperature sensor is attached to the back of the plate. Thermal isolation of the backside minimizes temperature gradients through the plate, ensuring correct readings of the front temperature. The isolation also serves to minimize convection gradients and keeps power consumption low, which is useful for battery powered operation in the field. We demonstrate surface blackbodies (200x200 mm2) with surface homogeneities as low as 0.1°C at 100°C. Homogeneous heating and low thermal mass provides for fast settling time and setup/pack-down time. The approach is scalable to larger sizes by tiling, enabling portable and foldable square-meter-size or larger devices.
Extracting information from low signal to noise ratio images poses significant challenges. Noise makes extracting spatial
features difficult, in particular if extraction of both large, smooth features at the same time as point-like features is
required. This work describes a new statistical approach, able to handle both simultaneously, with the capacity of
handling both positive and negative contrast signatures. The basic idea in this approach is that each pixel value can
represent underlying statistics to a varying degree, depending on how similar it is to samples taken close to it, spatially
and/or temporally. If the sample is similar to its surroundings, it is strongly filtered and also affects the filtering of
neighboring samples, but if it is significantly different, it will remain largely unfiltered and does not influence
neighboring pixel filtering. Simulations show that the filtering maintains energy conservation, significantly limits noise
and at the same time maintains signal integrity. The filter is found to adapt to noise characteristics and spatiotemporal
variations of the background. The technique is found to be well suited to rocket plume imaging, but is adaptable to a
broad range of other applications.
A novel imaging device based on tomographic reconstruction is presented. The imager is based on rotating an image of the scene onto a linear detector array, and then reconstructing a 2-dimensional image from the detector array signal using tomographic reconstruction techniques. Similar in many ways to the conical scan tomographic scanning (TOSCA) imager, the spin scan TOSCA imager features several improvements compared to its predecessors, mainly because the linear array covers the whole scene and hence in principle can collect 100% of the incoming photons. In addition to presenting the theory behind the device and its sensitivity to noise, non-uniformity and errors, an experimental uncooled mid-wave infrared imager demonstrator is also presented, together with images of test targets, both the final result as well as the incremental steps in the imaging reconstruction process.
The tomographic scanner (TOSCA) detects signals using line detectors scanning a scene at regularly distributed angles. These line scan signals are then processed to reconstruct 2-dimensional images. In the simplest form, a 1-axis rotating conical scan optics scans across a simple patterned reticle, the signal collection being done with a single pixel detector. Experimental mono- and multispectral cameras using this approach are demonstrated under varying illumination conditions. Of particular interest is the TOSCA system’s ability to handle and compensate for light sources modulated with a frequency higher than that of the frame rate. We also demonstrate for the first time a TOSCA imager operating in the infrared region. The device is put together using 3D-printed key parts and low cost optical components, leading to a very economical infrared camera.
In some applications of multi- or hyperspectral imaging, it is important to have a compact sensor. The most compact
spectral imaging sensors are based on spectral filtering in the focal plane. For hyperspectral imaging, it has been
proposed to use a "linearly variable" bandpass filter in the focal plane, combined with scanning of the field of view. As
the image of a given object in the scene moves across the field of view, it is observed through parts of the filter with
varying center wavelength, and a complete spectrum can be assembled. However if the radiance received from the object
varies with viewing angle, or with time, then the reconstructed spectrum will be distorted. We describe a camera design
where this hyperspectral functionality is traded for multispectral imaging with better spectral integrity. Spectral
distortion is minimized by using a patterned filter with 6 bands arranged close together, so that a scene object is seen by
each spectral band in rapid succession and with minimal change in viewing angle. The set of 6 bands is repeated 4 times
so that the spectral data can be checked for internal consistency. Still the total extent of the filter in the scan direction is
small. Therefore the remainder of the image sensor can be used for conventional imaging with potential for using motion
tracking and 3D reconstruction to support the spectral imaging function. We show detailed characterization of the point
spread function of the camera, demonstrating the importance of such characterization as a basis for image reconstruction.
A simplified image reconstruction based on feature-based image coregistration is shown to yield reasonable results.
Elimination of spectral artifacts due to scene motion is demonstrated.
The tomographic scanning (TOSCA) imager was invented by the author in 2003. Initially, the system was based on
reconstructing an image from the signal of a simple single pixel, conical scan FM-reticle sensor using tomographic
techniques. Although the system has been used for several decades for real-time tracking purposes, the imaging
properties of the single pixel conical scan reticle system was left undiscovered until recently, although multi-target
discrimination was demonstrated with multi-spectral versions of the system. The initial system presented by the author
demonstrated the ability to discriminate between multiple spots in the field of view in a fairly simple scenario.
Advances have been made in both theory and technology, mainly with the introduction of the nutating circular aperture
in the scanning optics, and the use of Fourier transform ramp filters during reconstruction, and TOSCA is in principle
found to be a perfect imaging system, only limited by practical aspects such as the number of angular scans, the spatial
sampling, noise and vibration. The simplicity of the hardware, together with the rapid advances in high performance,
low cost computing means the system has a potential for low-cost applications such as in expendable multi-spectral
thermal imagers. This paper will present the current state of the technology, including improvements in algorithms and reticle shapes, and
look at artefacts found in various images due to different geometries, as well as ways to handle these artefacts. Several
noise generating processes and their effects will be presented and illustrated with results from digital simulations.
Requirements for image processing in terms of computing power are investigated, together with the potential for
The use of expendable countermeasures is still found to be a viable choice for self protection against Man Portable Air
Defense Systems (MANPADS) due to their simplicity, low cost, flexibility, recent improvements in decoy technology,
the ability to handle multiple threats simultaneously and the off-board nature of these countermeasures. In civil aviation,
the risk of general hazards linked to the use of pyrotechnics is the main argument against expendable countermeasures,
whereas for military platforms, the limitation in capacity due to a limited number of rounds is often used as an argument
to replace expendable countermeasures by laser-based countermeasures. This latter argument is in general not
substantiated by modelling or figures of merit, although it is often argued that a laser based system allows for more false
alarms, hence enabling a more sensitive missile approach warning system.
The author has developed a model that accounts for the statistical effects of running out of expendable countermeasures
during a mission, in terms of the overall mission survival probability. The model includes key parameters of the missile
approach warning system (MAWS), and can handle multiple missile types and missile attack configurations, as well as
various statistical models of missile attacks. The model enables quantitative comparison between laser based and
expendable countermeasures, but also a dynamic optimization of the countermeasures in terms of whether to use small
or large countermeasure programs, as well as the dynamic tuning of MAWS key parameters to optimize the overall
performance. The model is also well suited for determination of the contributions of the different components of the
system in the overall survival probability.
Self-protection systems using expendable pyrotechnics have been in operational service for several decades, and still
enjoy a significant popularity on military platforms, due to potentially high efficiency, low cost and versatility. Recent
developments in advanced materials as well as spatial and temporal behaviour optimization using advanced simulation
tools also contribute to continued success against threat systems of ever-increasing sophistication.
One of the most significant drawbacks of these systems is the limited capacity of the countermeasures dispensers of
such a system. The risk of emptying the countermeasures dispensers leads to restrictions in the acceptable false-alarm-rate,
again leading to a reduced detection probability. The approaches for optimization known to the author have been
either one of Monte-Carlo simulations or a functional threat countering analysis. Neither of these brings insight into the
parameters relating the overall performance of the self-protection system against one missile attack and the overall
platform survivability on a mission.
In this work, a new model is presented where an overall survivability probability can be calculated and optimized,
including the effect of a limited dispenser capacity versus countermeasures program size as well as missile approach
warning systems key parameters, such as detection probability and false-alarm-rate. The model is extended to allow
independently variable missile attack- and false-alarm probabilities. Criteria for choosing optimal flare programs are
presented. It is shown that a dynamic update of the self protection system can enhance the performance of self protection
systems deploying expendable countermeasures. Monte-Carlo-simulations are shown to be in good
agreement with the model.
The use of the tomographic scanning (TOSCA) imaging technique is presented, and improvements using a rotating aperture are presented. The TOSCA imaging technique is based on tomographic imaging principles applied to the output from a conical scan reticle system, and allows for a simple, low cost system consisting of simple scanning optics, a single element detector and a signal-processing unit to act as an imaging sensor. Potential applications of the TOSCA seeker include missile seekers, smart munitions, and other devices using low-cost imagers.
A new imaging technique is introduced, based on tomographic imaging principles applied to the output from a conical scan reticle system. The concept allows for a simple, low cost system consisting of simple scanning optics, a single element detector and a signal-processing unit to act as an imaging sensor. Potential applications of the tomographic scanning imaging (TOSCA) seeker include missile seekers, smart munitions, and other devices using low-cost imagers.