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Sensor Modeling and Demonstration of a Multi-Object Spectrometer
for Performance-Driven Sensing
John P. Kerekesa, Michael D. Presnarab, Kenneth D. Fourspringa, Zoran Ninkova,
David R. Pogorzalaa, Alan D. Raisanena, Andrew C. Ricec, Juan R. Vasquezc,
Jeffrey P. Patela, Robert T. MacIntyrea, and Scott D. Browna1
Chester F. Carlson Center for Imaging Science
Rochester Institute of Technology
54 Lomb Memorial Drive
Rochester, NY 14623-5604
Air Force Institute of Technology
2950 Hobson Way
Wright-Patterson AFB, OH 45433-7765
Numerica Corporation
2661 Commons Blvd., Suite 210
Beavercreek, OH, USA 45431-3704
A novel multi-object spectrometer (MOS) is being explored for use as an adaptive performance-driven sensor that tracks
moving targets. Developed originally for astronomical applications, the instrument utilizes an array of micromirrors to
reflect light to a panchromatic imaging array. When an object of interest is detected the individual micromirrors imaging
the object are tilted to reflect the light to a spectrometer to collect a full spectrum. This paper will present example
sensor performance from empirical data collected in laboratory experiments, as well as our approach in designing optical
and radiometric models of the MOS channels and the micromirror array. Simulation of moving vehicles in a highfidelity, hyperspectral scene is used to generate a dynamic video input for the adaptive sensor. Performance-driven
algorithms for feature-aided target tracking and modality selection exploit multiple electromagnetic observables to track
moving vehicle targets.
Keywords: Hyperspectral scene simulation, physics-based modeling and simulation, DIRSIG, micromirror, DMD,
multi-object spectrometer, adaptive multimodal sensor, performance-driven sensor, feature-aided target tracking
1.1 Motivation and objectives
The U.S. Air Force Office of Scientific Research (AFOSR) is the sponsor for a Discovery Challenge Thrust (DCT) in the
area of integrated multimodal sensing, processing, and exploitation1. AFOSR is interested in basic research to conceive
adaptive multimodal electro-optical/radio-frequency (EO/RF) sensor concepts in a “performance-driven” context in
order to address problems of detecting, tracking, and identifying targets in highly cluttered, dynamic scenes. A
performance-driven integrated approach is a coupling of adaptive multimodal EO/RF sensing hardware with physicsbased modeling of target scene phenomenology, environmental interactions, data processing, and exploitation
algorithms. Modeling and simulation of a staring imager system should demonstrate the ability to capture multiple
Further author information: (Send correspondence to J.P.K. or J.R.V.)
J.P.K.: Email: [email protected], Telephone: 1 585 475-6996
J.R.V.: Email: [email protected], Telephone: 1 937 427-9725
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV,
edited by Sylvia S. Shen, Paul E. Lewis, Proc. of SPIE Vol. 7334, 73340J · © 2009 SPIE
CCC code: 0277-786X/09/$18 · doi: 10.1117/12.819265
Proc. of SPIE Vol. 7334 73340J-1
electromagnetic observables using a variety of sensing modalities, including spatial, spectral, polarimetric, radiometric,
and temporal within a broad wavelength region from the ultra-violet (UV) to the RF.
A fielded staring imaging sensor should be able to find and track individuals of interest in populated urban areas, detect
activity and materials indicative of improvised explosive device (IED) placement, and detect and identify threatening
space objects at long ranges. Thus, a novel multimodal detector design should utilize hyperspectral exploitation and
multimode fusing to enhance deeply-hidden, high-clutter target recognition by optimally exploiting the phenomenology
of multimodal target scene signatures. Innovation and development of a tunable, multimode, vertically integrated
(common sensor package), large-format staring focal plane array are required to accommodate the dynamic sensing
requirements dictated by the dynamic target scene.
1.2 Approach
The approach taken by the authors includes three major research veins, as shown in Figure 1. First, modeling of dynamic
scene phenomenology and incorporation of realistic moving target characteristics is required as a simulated input to test
a model of a performance-driven sensor. Second, basic research and integration of micro-electromechanical systems
(MEMS) devices, refractive/reflective optics, and focal plane arrays is required to achieve co-registered EO imagery,
video, polarization, and spectral sensing in a performance-driven adaptive optical sensor model. Third, a performancedriven algorithm is necessary to exploit multiple modalities of the sensor to track moving targets within the scene. The
joint performance effects of simultaneously varying parameters within each of these research veins can be evaluated
using various measures of effectiveness.
Research & Modeling
Performance Driven Sensing
De'ice & Optical System
Research & Morieling
Exploitation &
Sensor Conttol
Measures or
System Performance Modeling
Figure 1. Performance-Driven Sensor Approach Flowchart by RIT-Numerica Team
1.3 Overview of paper
We first provide an introduction to the dynamic scene modeling tools used as inputs in the simulation of a performancedriven sensor. We describe the Digital Imaging and Remote Sensing Image Generation (DIRSIG) software, a scenebuilding modeling and simulation tool that incorporates physics-based target scene phenomenology. The incorporation
of vehicular traffic into MegaScene to generate frames of video that include moving targets is also discussed. Next, we
present the modeling efforts of a micromirror array-based astronomical multi-object spectrometer (MOS) for the purpose
of designing a downward-looking MOS as a primary component of a performance-driven adaptive sensor.
Characterization of scattering when operating various micromirror array types within the adaptive sensor is discussed.
The performance-driven algorithm, used for tracking moving vehicles and selecting sensor modalities of the
micromirror-based adaptive sensor, is also presented. Finally, details of the future work required for an end-to-end
simulation of a performance-driven sensor model are provided.
Proc. of SPIE Vol. 7334 73340J-2
2.1 Hyperspectral image modeling in DIRSIG
The object tracking algorithms for this study were tested on a data set consisting of a series of synthetically-generated
image frames encoded into a video stream. These individual frames were generated using the DIRSIG model, a firstprinciples, physics-based synthetic image simulation software package2. The model has the ability to produce imagery in
a variety of modalities, including multispectral, hyperspectral, polarimetric, and LIDAR in the visible through the
thermal infrared regions of the electromagnetic spectrum.
The video data set consisted of a number of vehicles moving about the scene through time. The base scene used for this
was MegaScene 1, a high-fidelity recreation of region of northern Rochester, NY3. MegaScene 1 depicts a largely
residential, suburban-style neighborhood featuring a large number of houses, trees and a middle school. Within the scene
there is a main thoroughfare running in a north-south direction with several side streets and cul-de-sacs branching off.
An RGB rendering of the portion of MegaScene used here is depicted in Figure 2. (for color image see electronic version)
Figure 2. Example RGB frame of the test scene area
The DIRSIG model has the ability to utilize a specific radiometry solver on a per-material basis. This enables the
incorporation of bi-directional reflectance distribution functions (BRDFs) on appropriate materials. Although they were
not used for the simulation shown in Figure 2, BRDF models will be used in conjunction with the spectral reflectance
curves for the vehicle paints for this study. Since the vehicles are changing not just their location in the scene but also
their orientation with respect to the sun and sensor, the use of BRDF models will enable DIRSIG to reproduce solar glint
and other real-world phenomenology that would strain the vehicle tracking algorithm.
2.2 Vehicular traffic and video simulation
When simulating a video sequence with DIRSIG, the user has the ability to manually relocate scene content as a function
of time. However, with regard to vehicle motion, this approach is prohibitive because the user would be unable to
generate a realistic flow of traffic on a large scale. For this reason, vehicle motion was introduced into MegaScene 1
Proc. of SPIE Vol. 7334 73340J-3
through the use of the Simulation of Urban MObility (SUMO) traffic model, a “microscopic, space continuous and time
discrete traffic simulation” tool. The SUMO package allows the user to explicitly define any number of routes, on which
any number of vehicles can travel.
A road network of the MegaScene 1 area was created in order to integrate SUMO simulations with DIRSIG. Maps from
GoogleTM Maps were imported into Inkscape, a vector-based graphics package, and assigned to a background layer. The
network edges (lanes) and nodes (intersections) were then drawn atop the map and exported to SUMO in an Extensible
Markup Language (XML) format. Once the network had been generated, a series of routes were created for the vehicles
to travel along. In an attempt to create a simulation that resembled real-world traffic flow a large number of routes, most
of which traveled along the main thoroughfare for at least a portion of their journey, were generated. Once the network
and the routes were defined in SUMO, the simulation was performed and the output was reformatted into a series
DIRSIG inputs. A screen capture of the SUMO software simulating the traffic flow for this data set is shown in Figure 3.
Figure 3. Screen capture of SUMO simulating traffic flow
The video data set was designed to simulate a sensor operating at 10 Hz for a duration of 120 seconds. Each frame was
rendered as a hyperspectral cube of 61 bands spanning 0.4 – 1.0 μm at a resolution of 0.01 μm. The spatial extent of the
frames was approximately 700×400 m at a resolution of 0.75 m. These spectral and spatial parameters result in an image
file for each frame that is approximately 2 GB in size, resulting in a total of 2.5 TB of data for a two minute simulation
consisting of 1200 frames. The computing load was spread across a large number of CPU cores to parallelize the
process. This enabled the entire video to be simulated in approximately 2-3 days.
The system being studied for use as an adaptive performance-driven sensor is a previously constructed micromirror
array-based MOS that acquires panchromatic imagery of a scene as well as the full spectrum of selected objects of
We present the ongoing efforts to fully model the optical channels of a MOS, the input-output characteristics of a
commercial micromirror array, and novel micromirror design concepts.
Proc. of SPIE Vol. 7334 73340J-4
3.1 Multi-object spectrometer modeling
An adaptive multimodal optical sensor at the heart of a performance-driven sensor system is an imaging device with the
capability of interacting with a performance-driven algorithm to detect, identify, and track targets in real-time using
multiple modalities. A sensor that adaptively collects spatial, spectral, or polarization modalities was required for a highrate target tracker to exploit from frame to frame. Thus, a MOS was used as the starting point for this sensor design.
Various designs of multi-object spectrometers are present in the literature. The Near-Infrared Camera MOS4 of the
Hubble Space Telescope is a slitless design. The Gemini MOS5 requires custom manufacture of conventional lasermilled slit masks. The Hydra on the WIYN telescope’s MOS6 utilizes a robotic fiber-bundle positioner. The Infrared
MOS7 and the RITMOS8 both utilize a micromirror array for slit formation. A micromirror array is the most suitable for
slit creation in a performance-driven sensor due to its compactness and speed of commanded updates.
The RITMOS was chosen as the system to initiate modeling of an adaptive sensor. It was originally designed in 2003 as
an astronomical spectrometer and imager connected to a telescope for the purpose of Morgan-Keenan (M-K) spectral
classification of stars. It is sensitive within the M-K classical blue wavelength regime from 3900-4900 Å. Its optical
bench section is shown in Figure 4.
apttroscopy CCV
rotation stage
a barrel
ircialar baffle
to/jima for--4
p telescope
Erlifter wheel
ca!ibrat/on lamp
Figure 4. Top View of the RITMOS Optical Bench
The RITMOS utilizes a Texas Instruments Digital Micromirror Device (DMD) 848×600 array at the focal point of the 6element foreoptics assembly on its primary optical axis. Each of its individual 16 µm square mirrors is controlled to
deflect incident light into one of two output paths: an imaging channel or a spectrometry channel. Thus the DMD array
acts as a “light switch” to create on-the-fly slits within a 2-D pupil function, p[x,y]. The imaging channel consists of an
Offner relay and two fold mirrors that reimage the DMD onto a cooled 512×512 charge-coupled device (CCD) detector.
The utilization of a 5-position motorized filter wheel allows the collection of grayscale images that are either
panchromatic (clear aperture) or the output of red, green, or blue passband filters. The spectrometry channel consists of a
3-mirror reflective collimator, a 1200 lines/mm transmission grating, a 5-element reimager, and a cooled 4097×4130
CCD detector. Baffles are arranged in both channels to reduce stray light.
Acquisition of target spectra within a scene consists of five main steps. First, all incoming light is deflected by the DMD
array towards the imaging channel, which reimages the DMD through the Offner relay onto the imaging detector.
Second, the locations of targets of interest are selected using a software tool, and the corresponding micromirrors are
Proc. of SPIE Vol. 7334 73340J-5
deflected towards the spectroscopy channel. Third, the light is sent through the spectrometry channel collimator,
dispersed by the transmission grating, reimaged, exposed onto the spectrometry CCD detector, and differenced from a
dark frame. Since the dispersion is simply the convolution of the pupil function p[x,y] with the target’s spectral intensity
I[λ]|λ=y, replicas of p[x,y] are formed along the y-axis of the CCD centered on the row corresponding to the wavelength
of each Lorentzian-shaped emission line or merged across a continuous spectrum. Fourth, the incoming light path is cut
off, and light from a Krypton rare-gas calibration lamp is injected with a light-shaping diffuser into the optical axis
through the same micromirror slits and exposed onto the spectrometry channel’s CCD detector for wavelength-to-pixel
matching and extrapolation using the locations of seven known9 high-intensity Krypton emission lines between 42004600 Å. Finally, each row of CCD pixels corresponding to each of the multiple slits formed on targets of interest in the
scene is matched to the same row showing peaks of emission lines from the Krypton light, thus producing plots of
normalized spectral intensity vs. wavelength for each selected target.
A useful slit formed by the RITMOS for point source (e.g., star) spectrum collection is a 2×2 group of micromirrors. If a
linear relationship between wavelength (λ) and pixel number (x) is assumed across a detector row for λ ∈ [3900, 4900]
Å, then linear regression of the locations of the seven known locations of the Krypton emission lines yields the slopeintercept relationship λ = (0.7347x + 2918) Å for a 2×2 group of micromirrors at the exact center of the array. Here, the
dispersion of 0.7347 Å/pixel is valid near the center wavelength of 4400 Å, while the intercept of 2918 Å corresponds to
the wavelength at the detector row’s edge. Figure 5 shows the post-calibration results of the measured Krypton spectrum
in image (a), and the application of the extrapolated pixel-to-wavelength linear relationship to the spectra collected from
four types of paper colors using a Tungsten light source in image (b). Additional measurements of compact fluorescent
lamps (CFLs) and black lamps have shown Mercury emission line matches, and outdoor measurements have shown
Fraunhofer absorption line matches of elements in the upper layers of the sun. The point spread function (PSF) of the
refractive components of the imaging channel was also measured using a 2×2 group of micromirrors (i.e., an inverse slit)
to simulate a point source δ[x-x0,y-y0] directed through the Offner relay towards the imaging CCD.
Spectrometry Channel Output Spectrum of Tungsten Lamp Reflected from 4 Paper Colors
Spectrometry Channel Output Spectrum of Kr Lamp through 202 Center Slit
743194 IAI at 1907 pixels
White Paper
Blue Paper
Purple Paper
Red Paper
I 029
742756 [A] at 1845 ploelo
44634 [Al 012 03 p nets
4376 IA at
at 2156 pIxels
.5 IA] 052090 PiXelS
3627101011966 PXeIS
Wavelength [Al lTopl Spectrometry CCO Poe 6064097 Total (906090
Wavelength [A] (Top); Spectrometry CCD Pixel # of 4097 Total (Bottom)
Figure 5. (a) Measured Krypton lamp spectrum showing seven emission lines used for calibration.
(b) Measured Tungsten lamp spectrum reflected from four different paper colors. (for color image see electronic version)
Optical modeling10,11 of the RITMOS subcomponents (foreoptics lens, Offner relay and fold mirrors, filter wheel,
collimator, transmission grating, and reimager lens) was accomplished using Lambda Research Corporation’s OSLO
Premium Edition. Work is in progress to integrate these models with the micromirror array model discussed in the next
section, with an end goal to create a modulation transfer function (MTF) model that can generate the outputs of both
imaging and spectrometry CCDs given a simulated input scene.
Radiometric modeling12 of the RITMOS provides insight into the efficiency of every subcomponent of the system. The
baseline parameters of exoatmospheric irradiance, atmospheric transmission, and target reflectivity can be used to derive
the sensor-reaching radiance. Transmissions and positions of the optically modeled RITMOS subcomponents were used
to determine the irradiance onto each detector pixel. Consideration of each CCD’s integration time, readout time,
Proc. of SPIE Vol. 7334 73340J-6
detector pixel area, and quantum efficiency yielded estimates of the respective signal-to-noise ratio. This model will be
used during iteration of the design concepts.
3.2 Micromirror array modeling
A micromirror is either an electrostatically driven or thermally actuated microelectromechanical system (MEMS) device.
Texas Instruments (TI) has continuously revised their Digital Micromirror Device (DMD) for over 35 years as the
technological advancements of the IC manufacturing industry have evolved. The DMD has been used in a multitude of
optical designs13. The original application for the DMD was a spatial light modulator (SLM) for Digital Light Processing
(DLP®) televisions and projectors14. Each individual DMD tilts along the diagonal to direct the light from a metal-halide
or mercury arc lamp source to either a light trap or a projection lens, as demonstrated in images (a) and (b) of Figure 6,
respectively. Black, white, and intermediate grey levels across the imaging field are produced by temporally modulating
the length of time that the mirror is in its “on” state to produce varying grey levels as observed by the human visual
system. There is also a finite stabilization time on the order of 18 µs for both on and off transitions. In most projector
applications the incoming wavefront is collimated. (for color image see electronic version of paper)
Figure 6. DMD Modeling Showing a Collimated Incoming Wavefront (Green Rays) and Specularly Reflected Output (Red Rays)
(a) The “Off” state in the DMD projector application directing the light towards a light trap.
(b) The “On” state in the DMD projector application directing the light towards the projection optics.
(c) The “imaging” switch position in the DMD MOS application directing the light towards the imaging channel.
(d) The “spectrometry” switch position in the DMD MOS application directing the light towards the spectrometer channel.
The DMD array is located at the imaging plane of the foreoptics in the RITMOS system. The DMD is at the heart of the
RITMOS system and is in many respects the limiting factor. One of the primary differences in the MOS switching
application is a converging beam impinging on the micromirror array rather than a collimated wavefront. The incoming
cone of rays is limited by the angle in which the mirrors can steer the wavefront to the imaging or spectrometry channel
of the MOS, as demonstrated in images (c) and (d) of Figure 6. This sets a practical limit for the speed of the foreoptics
because the reflected wavefront cannot overlap the incoming wavefront. However, the greater the angle the mirrors tilt,
Proc. of SPIE Vol. 7334 73340J-7
the lower the fill factor of the micromirror array. The fill factor becomes very important once scattering is considered
because as the space between the mirrors increases, more light will enter the region below the mirrors leaving the
possibility of more stray light in the system. Therefore, a more complete model of the micromirrors was generated to
fully model all of the phenomena associated with the stray light in the micromirror optical switch. Finally, in a non-ideal
mirror such as the TI DMD, the central post or “via” that supports the mirror above the hinge acts as a severe scattering
center. Thus, TI has reduced the via’s size in subsequent mirror designs to improve contrast ratios in projection systems.
The optical software package used to perform stray light modeling was Photon Engineering's FRED. FRED is a
powerful optical design prototyping software package that utilizes non-sequential ray tracing. Non-sequential ray tracing
is essential to modeling the scattering and diffraction effects of the multiple surfaces and materials in a DMD. Figure 7
shows an example of a detailed Texas Instruments micromirror 3-D model showing the reflected wavefront from a
converging beam on a micromirror array. Detailed scatter measurements will be collected to obtain a baseline scatter
model to use within the optical simulation software package to verify the accuracy of the current model. These scattering
measurements will be made in a novel optical setup to extract the diffuse and specular components of reflection as a
function of position within the micromirror. The most important outcome of this detailed modeling is the methodology
that will be used to effectively model future micromirror designs optimized for MOS applications. Obtaining the
spectral/polarized transfer function this micromirror device is the gateway to predicting accurate performance of future
MOS systems. (for color image see electronic version of paper)
Figure 7. A DMD array model used for scattering simulations. The incoming converging wavefront (not plotted) is focused onto a 3×3
group of micromirrors. A single mirror is pointed towards the spectrometry channel (green output rays). All other mirrors are pointed
towards the imaging channel (red output rays).
3.3 Micromirror design concepts
The RIT Semiconductor and Microsystems Fabrication Laboratory (SMFL) developed a mechanical and optical model
for the TI DLP® DMD to be used in validation of our full-system optical throughput model. A collection of new
complementary metal oxide semiconductor (CMOS) process compatible micromirror devices have been designed and
mechanically modeled in order to investigate the behavior of complete optical systems incorporating these devices.
Imaging modes can be investigated, and image defects induced by the mirror plane can be determined before difficult
and expensive device fabrication processes are begun. In Figure 8, the structure color of image (e) indicates the
temperature of a thermally-driven micromirror device, while the color in all other images indicates total translation from
a rest position. The micromirror device in image (d) is a candidate for mixed beam steering / Fabry-Perot etalon filtering
applications. A micromirror device with very large angular, vertical, and lateral deflection capabilities is shown in image
(f). Designs shown in images (b), (c), and (e) have been successfully prototyped at RIT, while those shown in images (d)
and (f) are purely design concepts at this time. (for color image see electronic version of paper)
Proc. of SPIE Vol. 7334 73340J-8
Figure 8. (a) Model of Texas Instruments DLP®-technology DMD array section
(b) Model of simple 1-axis electrostatically driven torsion spring mirror
(c) Model of 2-axis electrostatically driven torsion spring mirror supported at all four corners
(d) Model of a vertically oriented thermally or electrostatically driven spring device supported at all four corners
(e) Model of a single hinge 1-axis micromirror driven by differential thermal expansion
(f) Model of a vertically-oriented thermally driven spring supported micromirror device
Performance-driven sensing is a process which conditions the design, employment, and – of particular interest to this
study – adaptation of an instrument on exploitation results. Tracking moving vehicles within challenging environments
through remote, persistent, hyperspectral imagery (HSI) data is an emerging field of research. Thus, an experiment has
been conceived which applies performance-driven sensing techniques to a synthesized, adaptive, multimodal DMDbased instrument15. The goal is to maximize overall track-level performance by carefully choosing which pixels should
collect HSI data at which times. This section briefly describes the motivation and fundamentals behind feature-aidedtracking, and discusses modality selection as a means of real-time instrument adaptation.
4.1 Tracking techniques
In this context, tracking is the process of estimating the kinematic state of multiple, agile ground vehicles in the presence
of clutter, dropped measurements, confusable vehicles, environmental occlusion, and ambiguous movement. A critical
phase in the tracking process is the association of new measurements with existing tracks. The tracking system under test
employs various high-level association constructs to allow for statistics-based gating, multidimensional assignment, and
deferred decision-making. However, the fundamental cost Ci , j to associate a track i with a measurement j is the key, as
shown in Equation 1:
Ci , j = μ K C% iK, j + ∑ μ Fn C% iF, nj
Here, C%iK, j is a normalized kinematic cost based on the Mahalanobis distance, and C%iF, nj are likewise normalized costs
based on statistical distances in an n-dimensional feature-space. The weighting terms μ establish the relative importance
of the kinematic and feature association costs. It is well known that HSI instruments provide high-saliency feature
Proc. of SPIE Vol. 7334 73340J-9
measurements for many classes of ground vehicles. Hence, an HSI feature-aided tracking system has the potential to
more accurately associate measurements with tracks and subsequently perform with longer overall track life and higher
track purity metrics. These assumptions are predicated on the availability of feature data, i.e. full spectral information,
for both measurements and track state. While many realizable instruments always collect full HSI information
throughout their fields of view, there is generally a design-time tradeoff such as ground sample distance or scan rate
which makes tracking difficult. This experiment focuses on adaptive modality selection of an instrument which collects
high-rate panchromatic data for the sake of tracking, but allows some pixels to collect HSI data as required.
4.2 Modality selection
The goal of spatial sampling is to determine which pixels will collect HSI data. The utility function is a linear
combination of heuristic values and assigns a value to the usefulness of collecting HSI data at each pixel. Let Uij(t)
represent the utility of obtaining HSI data at the ijth pixel at time t and given by Equation 2:
U ij (t ) = C DU ijD (t ) + C NU ijN (t ) + C AU ijA (t ) + C M U ijM (t ) + C ℑU ijℑ (t )
s.t. U ijΦ (t ) ∈ [0,1],
Φ ∈ {D, N , A, M , ℑ},
= 1,
CΦ ≥ 0 ∀ Φ
The values of C are the relative importance or weighting of the different utility components which are defined as:
U ij (t ): Default value that every target of interest receives which gradually decreases towards 0 as we consider pixels
farther from the predicted location of the target track.
U ij (t ) : New model utility which is a function of the appearance of new or reacquired targets that need to be sampled in
order to build a target feature model.
U ij (t ): Association utility defined for closely spaced targets where track state and the related uncertainty provide a
measure of association ambiguity.
U ij (t ): Missed measurement utility which is a function of the number of missed detections for the kinematic tracker due
to occlusion or shadow.
U ij (t ): Model age which is a function of the Time since the last spectral model measurement was incorporated.
Additional constraints are applied to the modality selection algorithm to accommodate instrument limitations. For
instance: preventing spectral/spatial overlap on the spectroscopy array due to conflicting pixel HSI requests. As target
tracking scenarios increase in complexity and sensor resources begin to stretch thin – e.g., there are far fewer
opportunities to collect HSI data than requirements to do so – it becomes increasingly important to have an optimal, realtime approximation to the utility function Uij(t). A genetic algorithm approach has been applied16, recovering values for
C based on representative training data.
5.1 Work accomplished to date
This paper summarizes the basic research performed by the authors in the first year of a three-year AFOSR DCT grant.
Vehicle movement has been added to a hyperspectral DIRSIG scene, producing video frames as seen by a static sensor
platform. Imagery and spectra were collected on the RITMOS to aid the design of its optical and radiometric modeling
efforts. Initial scattering models of micromirror arrays were completed, and novel micromirror design concepts have
been studied and modeled. Initial tests of a performance-driven algorithm on direct DIRSIG video frames have
demonstrated HSI feature-aided tracking performance.
Proc. of SPIE Vol. 7334 73340J-10
5.2 Dynamic scene modeling future work
Future iterations of DIRSIG-generated video data sets will have a number of enhancements. Near-term considerations
include, as alluded to in Section 2.1, the addition of BRDF parameters to the vehicle paint spectra, introducing a moving
sensor platform, and the incorporation of video artifacts such as dropped frames and MPEG compression that often arise
in a real-world downlinked video scenario.
A long-term goal is to simulate a video product generated by a suite of sensors that are polarization-sensitive. Image
products generated from such a sensor, such as Degree and Angle of Polarization, can add a new dimension of
discriminability to the tracking algorithm by keying on such vehicle characteristics as surface roughness and orientation.
A framework for simulating polarimetric data has been established for DIRSIG17, and the model has been shown to be
able to accurately reproduce a nominal polarized scene18. However before such a data set can be generated, polarized
BRDF (pBRDF) models must be attributed to both the static and dynamic scene content. Such models are currently
being attributed to the static elements of MegaScene 1, and nominal data sets are being generated. Once the entire scene
has been attributed, and once an appropriately diverse array of models have been identified and attributed to the vehicles,
polarized video products will be generated and tested against.
5.3 Adaptive sensor modeling future work
The fidelity of the adaptive sensor model will be vastly increased from the baseline RITMOS design. Modifications to
the foreoptics model will allow enhanced field-of view and zooming capabilities. Multiple transmission gratings (e.g., on
a selectable turret) and calibration lamps with appropriate spectral emission lines will be added to increase the
wavelength region to 0.4-2.5 µm. Arrays of novel micromirror devices will be simulated within the adaptive sensor
model to reduce scattering and increase contrast. Polarization filters will contribute another sensing modality.
5.4 Performance-driven algorithm future work
Future performance-driven algorithm work will include refinement of the track feature model, particularly as it relates to
the utility function element U ij (t ) . Integration with the adaptive sensor model will allow for an end-to-end
demonstration of multi-modal target tracking using a dynamic video scene simulated by DIRSIG and SUMO.
This material is based on research sponsored by the Air Force Office of Scientific Research (AFOSR) under agreement
number FA9550-08-1-0028 (AFOSR-BAA-2007-08). The U.S. Government is authorized to reproduce and distribute
reprints for Governmental purposes notwithstanding any copyright notation thereon.
Scene simulation was accomplished using DIRSIG version 4.2.2, developed at RIT ( Traffic
simulation was accomplished using SUMO version 0.10.1, developed by the German Aerospace Centre
( Optical modeling of RITMOS components was accomplished using OSLO Premium
Edition revision 6.4.6, donated by Lambda Research Corporation ( for thesis research. FRED
Optimum version 7.10, produced by Photon Engineering LLC ( was used to model the
micromirror arrays and simulate scattering. COMSOL Multiphysics®, developed by COMSOL AB
( was used to model the various micromirror design concepts.
The views expressed in this article are those of the authors and do not reflect the official policy or position of the United
States Air Force, the Department of Defense, or the U.S. Government.
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