This PDF file contains the front matter associated with SPIE Proceedings Volume 8711, including the Title Page, Copyright Information, Table of Contents, and the Conference Committee listing.

Classification of human and animal targets imaged by a linear pyroelectic array senor presents some unique challenges especially in target segmentation and feature extraction. In this paper, we apply two approaches to address this problem. Both techniques start with the variational energy functional level set segmentation technique to separate the object from background. After segmentation, in the first technique, we extract features such as texture, invariant moments, edge, shape information, and spectral contents of the segmented object. These features are fed to classifiers including Naïve Bayesian (NB), and Support Vector Machine (SVM) for human against animal classification. In the second technique, the speeded up robust feature (SURF) extraction algorithm is applied to the segmented objects. A code book technique is used to classify objects based on SURF features. Human and animal data acquired-using the pyroelectric sensor in different terrains, are used for performance evaluation of the algorithms. The evaluation indicates that the features extracted in the first technique in conjunction with the NB classifier provide the highest classification rates. While the SURF feature plus code book approach provides a slightly lower classification rate, it provides better computational efficiency lending itself to real time implementation.

The challenge of rapid footstep detection and classification in remote locations has long been an important area of study for defense technology and national security. Also, as the military seeks to create effective and disposable unattended ground sensors (UGS), computational complexity and power consumption have become essential considerations in the development of classification techniques. In response to these issues, a research project at the Flexible Display Center at Arizona State University (ASU) has experimented with footstep classification using the matching pursuit decomposition (MPD) time-frequency analysis method. The MPD provides a parsimonious signal representation by iteratively selecting matched signal components from a pre-determined dictionary. The resulting time-frequency representation of the decomposed signal provides distinctive features for different types of footsteps, including footsteps during walking or running activities. The MPD features were used in a Bayesian classification method to successfully distinguish between the different activities. The computational cost of the iterative MPD algorithm was reduced, without significant loss in performance, using a modified MPD with a dictionary consisting of signals matched to cadence temporal gait patterns obtained from real seismic measurements. The classification results were demonstrated with real data from footsteps under various conditions recorded using a low-cost seismic sensor.

Iris recognition is the most reliable method in personal identification. However, the current fixed-focus iris imaging system has small depth of field (DOF), which limits the wide application of the iris recognition system. This paper presents the design method and optimization of a phase mask based iris imaging system. Through wavefront coding, it can extend the DOF and enhance the convenience of iris image acquisition. Through analyzing the modulation transfer function and optical parameters of the cubic phase mask, we can get the wavefront coding iris imaging system’s optimal parameter and it’s structure. Experimental results show that the cubic phase mask based iris imaging system has larger DOF and better imaging performance.

Active mode standoff measurement using infrared spectroscopy were carried out in which the angle between target and the source was varied from 0-70° with respect to the surface normal of substrates containing traces of highly energetic materials (explosives). The experiments were made using three infrared sources: a modulated source (Mod-FTIR), an unmodulated source (UnMod-FTIR) and a scanning quantum cascade laser (QCL), part of a dispersive mid infrared (MIR) spectrometer. The targets consisted of PENT 200 μg/cm² deposited on aluminum plates placed at 1 m from the sources. The evaluation of the three modalities was aimed at verifying the influence of the highly collimated laser beam in the detection in comparison with the other sources. The Mod-FTIR performed better than QCL source in terms of the MIR signal intensity decrease with increasing angle.

Acoustical sniper positioning is based on the detection and direction-of-arrival estimation of the shockwave and the muzzle blast acoustical signals. In real-life situations, the detection and direction-of-arrival estimation processes is usually performed under the influence of background noise sources, e.g., vehicles noise, and might result in non-negligible inaccuracies than can affect the system performance and reliability negatively, specially when detecting the muzzle sound under long range distance and absorbing terrains. This paper introduces a multi-band spectral subtraction based algorithm for real-time noise reduction, applied to gunshot acoustical signals. The ballistic shockwave and the muzzle blast signals exhibit distinct frequency contents that are affected differently by additive noise. In most real situations, the noise component is colored and a multi-band spectral subtraction approach for noise reduction contributes to reducing the presence of artifacts in denoised signals. The proposed algorithm is tested using a dataset generated by combining signals from real gunshots and real vehicle noise. The noise component was generated using a steel tracked military tank running on asphalt and includes, therefore, the sound from the vehicle engine, which varies slightly in frequency over time according to the engine’s rpm, and the sound from the steel tracks as the vehicle moves.

Counter sniper systems rely on the detection and parameter estimation of the shockwave and the muzzle blast in order to determine the sniper location. In real-world situations, these acoustical signals can be disturbed by natural phenomena like weather and climate conditions, multipath propagation effect, and background noise. While some of these issues have received some attention in recent publications with application to gunshot acoustics, the multipath propagation phenomenon whose effect can not be neglected, specially in urban environments, has not yet been discussed in details in the technical literature in the same context. Propagating sound waves can be reflected at the boundaries in the vicinity of sound sources or receivers, whenever there is a difference in acoustical impedance between the reflective material and the air. Therefore, the received signal can be composed of a direct-path signal plus N scaled delayed copies of that signal. This paper presents a discussion on the multipath propagation effect and its impact on the performance and reliability of sniper positioning systems. In our formulation, propagation models for both the shockwave and the muzzle blast are considered and analyzed. Conclusions following the theoretical analysis of the problem are fully supported by actual gunshots acoustical signatures.

Ground surveillance is normally performed by human assets, since it requires visual intelligence. However, especially for military operations, this can be dangerous and is very resource intensive. Therefore, unmanned autonomous visualintelligence systems are desired. In this paper, we present an improved system that can recognize actions of a human and interactions between multiple humans. Central to the new system is our agent-based architecture. The system is trained on thousands of videos and evaluated on realistic persistent surveillance data in the DARPA Mind’s Eye program, with hours of videos of challenging scenes. The results show that our system is able to track the people, detect and localize events, and discriminate between different behaviors, and it performs 3.4 times better than our previous system.

McQ has developed, tested, and is supplying to Unattended Ground Sensor (UGS) customers a new radar sensor. This radar sensor is designed for short range target detection and classification. The design emphasis was to have low power consumption, totally automated operation, a very high probability of detection coupled with a very low false alarm rate, be able to locate and track targets, and have a price compatible with the UGS market. The radar sensor complements traditional UGS sensors by providing solutions for scenarios that are difficult for UGS. The design of this radar sensor and the testing are presented in this paper.

In today’s conjuncture, the terrorist activities are the most compelling issue for the defence forces in maintaining homeland security. Especially, the terrorist elements that penetrate the homeland may give harm. This harm can be minimized by preventing the terrorist penetrations from homeland borders. In counter terrorism, having Intelligence, Surveillance and Reconnaissance (ISR) capability and using this capability by twenty four hours is deterrence for the terrorist groups. Aerostats emerge as the ideal platform which can provide this capability. Aerostats are unmanned and aerodynamically shaped balloons that are stayed in the air, fixed to the ground by steel cable(s). The aerostat is made of a large fabric envelope that is filled with nonflammable helium gas, which provides the lifting force. The cables also serve to supply the electrical power to the aerostat systems, and for data relay between the aerostat and the ground station. Aerostats are different from the other manned and Unmanned Aerial Vehicles (UAVs) because of aerostats’ capabilities such as cost effectiveness, long endurance and high resolution image transmission. Especially having uninterrupted image transmission and surveillance capabilities is important to be advantageous in counter terrorism. In this article, a short definition of terrorism has been given and then the importance of ensuring the homeland border security has been emphasized in counter terrorism. In addition, the questions of “what are the technical capabilities, the usage areas and the purposes of aerostats?” will be introduced as a result of literature review. Finally the strengths and weaknesses of aerostats, opportunities and threats for the near future will be introduced by using “SWOT” analysis method.

The OptaSense® Distributed Acoustic Sensing (DAS) technology can turn any cable with single-mode optical fiber into a very large and densely sampled acoustic/seismic sensor array—covering up to a 50 km aperture per system with "virtual" sensor separations as small as 1 meter on the unmodified cable. The system uses Rayleigh scattering from the imperfections in the fiber to return the optical signals measuring local fiber strain from seismic or air and water acoustic signals. The scalable system architecture can provide border monitoring and high-security perimeter and linear asset protection for a variety of industries—from nuclear facilities to oil and gas pipelines. This paper presents various application architectures and system performance examples for detection, localization, and classification of personnel footsteps, vehicles, digging and tunneling, gunshots, aircraft, and earthquakes. The DAS technology can provide a costeffective alternative to unattended ground sensors and geophone arrays, and a complement or alternative to imaging and radar sensors in many applications. The transduction, signal processing, and operator control and display technology will be described, and performance examples will be given from research and development testing and from operational systems on pipelines, critical infrastructure perimeters, railroads, and roadways. Potential new applications will be discussed that can take advantage of existing fiber-optic telecommunications infrastructure as “the sensor”—leading to low-cost and high-coverage systems.

This paper describes research and testing of a representative group of high priority body worn and implantable personal medical electronic devices (PMEDs) for exposure to millimeter wave (MMW) advanced imaging technology (AIT) security systems used at airports. The sample PMEDs included in this study were implantable cardiac pacemakers, ICDs, neurostimulators and insulin pumps. These PMEDs are designed and tested for susceptibility to electromagnetic interference (EMI) under the present standards for medical device electromagnetic compatibility (EMC). However, the present standards for medical equipment do not address exposure to the much higher frequency fields that are emitted by MMW security systems. Initial AIT emissions measurements were performed to assess the PMED and passenger exposures. Testing protocols were developed and testing methods were tailored to the type of PMED. In addition, a novel exposure simulation system was developed to allow controlled EMC testing without the need of the MMW AIT system. Methodology, test results, and analysis are presented, along with an assessment of the human exposure and risks for PMED users. The results on this study reveal no effects on the medical devices from the exposure to the MMW security system. Furthermore, the human exposure measurements and analysis showed levels well below applicable standard, and the risks for PMED users and others we assessed to be very low. These findings apply to the types of PMEDs used in the study though these findings might suggest that the risks for other, similar PMEDs would likely be similar.

Given a specific scenario for the border control problem, we propose a dynamic data-driven adaptation of the associated sensor network via embedded software agents which make sensor network control, adaptation and collaboration decisions based on the contextual information value of competing data provided by different multi-modal sensors. We further propose the use of influence diagrams to guide data-driven decision making in selecting the appropriate action or course of actions which maximize a given utility function by designing a sensor embedded software agent that uses an influence diagram to make decisions about whether to engage or not engage higher level sensors for accurately detecting human presence in the region. The overarching goal of the sensor system is to increase the probability of target detection and classification and reduce the rate of false alarms. The proposed decision support software agent is validated experimentally on a laboratory testbed for multiple border control scenarios.

This paper describes the concept and method in designing and developing a unique security system apparatus that will counter unauthorized personnel: to deny access to or occupy an area or facility, to control or direct crowd or large groups, and to incapacitate individuals or small groups until they can be secured by military or law enforcement personnel. The system exploits Tesla coil technology. Application of basic engineering circuit analysis and principle is demonstrated. Transformation from classical spark gap method to modern solid state design was presented. The analysis shows how the optimum design can be implemented to maximize performance of the apparatus. Discussion of the hazardous effects of electrical elements to human physiological conditions was covered. This serves to define guidelines in implementing safety limits and precautions on the performance of the system. The project is strictly adhering towards non-lethal technologies and systems.

In this paper, we propose new sequential methods for detecting port-scan attackers which routinely perform random "portscans" of IP addresses to find vulnerable servers to compromise. In addition to rigorously control the probability of falsely implicating benign remote hosts as malicious, our method performs significantly faster than other current solutions. Moreover, our method guarantees that the maximum amount of observational time is bounded. In contrast to the previous most effective method, Threshold Random Walk Algorithm, which is explicit and analytical in nature, our proposed algorithm involve parameters to be determined by numerical methods. We have introduced computational techniques such as iterative minimax optimization for quick determination of the parameters of the new detection algorithm. A framework of multi-valued decision for detecting portscanners and DoS attacks is also proposed.

The capability to positively and covertly identify people at a safe distance, 24-hours per day, could provide a valuable advantage in protecting installations, both domestically and in an asymmetric warfare environment. This capability would enable installation security officers to identify known bad actors from a safe distance, even if they are approaching under cover of darkness. We will describe an active-SWIR imaging system being developed to automatically detect, track, and identify people at long range using computer face recognition. The system illuminates the target with an eye-safe and invisible SWIR laser beam, to provide consistent high-resolution imagery night and day. SWIR facial imagery produced by the system is matched against a watch-list of mug shots using computer face recognition algorithms. The current system relies on an operator to point the camera and to review and interpret the face recognition results. Automation software is being developed that will allow the system to be cued to a location by an external system, automatically detect a person, track the person as they move, zoom in on the face, select good facial images, and process the face recognition results, producing alarms and sharing data with other systems when people are detected and identified. Progress on the automation of this system will be presented along with experimental night-time face recognition results at distance.

Long-range surveillance systems are typically used in rural areas for detecting and tracking illegal border crossings, trafficking and drug activity. These systems commonly deploy mast or tower-based surveillance systems equipped with thermal infrared cameras, which have the advantage of providing early warnings and increasing the range of observation. However, these systems are subject to high frequency vibration due to slight wind or wind gusts, which is difficult to correct mechanically. In order to identify the border activity, it is critical for the vision system to robustly detect the objects in the scene, classify the objects and track the detected targets. The performance of these post-processing algorithms is known to suffer if the video is not properly stabilized.

Surveillance systems in rural areas, particularly in thermal band, pose several unique challenges to video stabilization algorithms. First, the scene rarely contains man-made objects. Water surface, trees and forests present very low contrast and ambiguous textures such that stabilization algorithms struggle to consistently and repeatedly extract distinctive corners and features. Second, even if the system captures certain human activities or structural objects in the scene, the video typically lacks sharpness in the background due to the motion blur at the long range. In this research paper, we propose a biologically-inspired, robust and compact video motion stabilization algorithm, which is ideal for rural areas. Our novel algorithm is compared quantitatively with other competing algorithm (SURF) in terms of robustness and performance. Finally, we evaluate the resource usage on FPGA platforms.

Autonomous operations of search and rescue (SaR) robots is an ill posed problem, which is complexified by the dynamic disaster recovery environment. In a typical SaR response scenario, responder robots will require different levels of processing capabilities during various parts of the response effort and will need to utilize multiple algorithms. Placing these capabilities onboard the robot is a mediocre solution that precludes algorithm specific performance optimization and results in mediocre performance. Architecture for an ad-hoc, deployable cloud environment suitable for use in a disaster response scenario is presented. Under this model, each service provider is optimized for the task and maintains a database of situation-relevant information. This service-oriented architecture (SOA 3.0) compliant framework also serves as an example of the efficient use of SOA 3.0 in an actual cloud application.

Passive infrared (PIR) sensors are widely used as a part of unattended ground sensor suite for situational awareness. Currently, the PIR sensor is mainly used as a wakeup sensor for the imaging sensor in order to conserve power. Since the PIR sensor mainly responds to the thermal radiation from the target, animals in the vicinity of the sensor can cause many false alarms. The number of false alarms can be cut drastically, if the target’s size can be estimated and a decision is made based on target size. For example, if the target is 5 ft 9 in tall and 1.5 ft wide, it is most likely a human being as opposed to an animal. In this paper, we present a technique to estimate target size using two PIR sensors with Fresnel lens arrays. One of the PIR sensors is mounted such that its Fresnel zones are horizontal to the ground, and the second PIR sensor is mounted such that its Fresnel zones are at a slant angle to the horizontal plane. The former is used to estimate the width/length, while the latter is used to estimate the height of the target. The relative signal strength between the two sensors is used to estimate the distance of the target from the sensor. The time it takes to cross the Fresnel zones is used to estimate the speed of the target. The algorithm is tested using the data collected in the woods, where several animals are observed roaming.

Software tools for Social Network Analysis (SNA) are being developed which support various types of analysis of social networks extracted from social media websites (e.g., Twitter). Once extracted and stored in a database such social networks are amenable to analysis by SNA software. This data analysis often involves searching for occurrences of various subgraph patterns (i.e., graphical representations of entities and relationships). The authors have developed the Graph Matching Toolkit (GMT) which provides an intuitive Graphical User Interface (GUI) for a heuristic graph matching algorithm called the Truncated Search Tree (TruST) algorithm. GMT is a visual interface for graph matching algorithms processing large social networks. GMT enables an analyst to draw a subgraph pattern by using a mouse to select categories and labels for nodes and links from drop-down menus. GMT then executes the TruST algorithm to find the top five occurrences of the subgraph pattern within the social network stored in the database. GMT was tested using a simulated counter-insurgency dataset consisting of cellular phone communications within a populated area of operations in Iraq. The results indicated GMT (when executing the TruST graph matching algorithm) is a time-efficient approach to searching large social networks. GMT’s visual interface to a graph matching algorithm enables intelligence analysts to quickly analyze and summarize the large amounts of data necessary to produce actionable intelligence.

Autonomous navigation around power lines in a complex urban environment is a critical challenge facing small unmanned aerial vehicles (SUAVs). As part of an ongoing development of an electric and magnetic field sensor system designed to provide SUAVs with the capability to sense and avoid power transmission and distribution lines by monitoring their electric and magnetic field signatures, we have performed field measurements and analysis of power-line signals. We discuss the nature of the power line signatures to be detected, and optimal strategies for detecting these signals amid SUAV platform noise and environmental interference. Based on an analysis of measured power line signals and vehicle noise, we have found that, under certain circumstances, power line harmonics can be detected at greater range than the fundamental. We explain this phenomenon by combining a model of power line signal nonlinearity with the quasi-static electric and magnetic signatures of multiphase power lines.

Drones and Remotely Piloted Vehicles are types of Unmanned Aerial Vehicles. UAVs began to be used with the war of Vietnam, they had a great interest when Israel used them in Bekaa Valley Operations of 1982. UAVs have been used by different countries with different aims with the help of emerging technology and investments. In this article, in the context of areas of UAV usage in national security, benefits and disadvantages of UAVs are put forward. Particularly, it has been evaluated on the basis of cost-effectiveness by focusing the use of UAV in the border security. UAVs have been studied by taking cost analysis, procurement and operational costs into consideration. Analysis of effectiveness has been done with illegal passages of people and drugs from flight times of UAVs. Although the procurement cost of the medium-level UAVs is low, its operational costs are high. For this reason, the idea of less costly alternative systems have been revealed for the border security. As the costs are reduced to acceptable level involving national security and border security in future with high-technology products in their structure, it will continue to be used in an increasing proportion.

A next generation of Smart antennas with point-to-point communication and jam, spoof protection capability by verification of spatial position is offered. A directional antenna array (DAA) with narrow irradiation beam provides counter terrorism protection for communications, data link, control and GPS. Communications are “invisible” to guided missiles because of 20 dB smaller irradiation outside the beam and spatial separation. This solution can be implemented with current technology. Directional antennas have higher gain and can be multi-frequency or have wide frequency band in contrast to phase antenna arrays. This multi-directional antenna array provides a multi-functional communication network and simultaneously can be used for command control, data link and GPS.

Modeling real-world scenarios is a challenge for traditional social science researchers, as it is often hard to capture the intricacies and dynamisms of real-world situations without making simplistic assumptions. This imposes severe limitations on the capabilities of such models and frameworks. Complex population dynamics during natural disasters such as pandemics is an area where computational social science can provide useful insights and explanations. In this paper, we employ a novel intent-driven modeling paradigm for such real-world scenarios by causally mapping beliefs, goals, and actions of individuals and groups to overall behavior using a probabilistic representation called Bayesian Knowledge Bases (BKBs). To validate our framework we examine emergent behavior occurring near a national border during pandemics, specifically the 2009 H1N1 pandemic in Mexico. The novelty of the work in this paper lies in representing the dynamism at multiple scales by including both coarse-grained (events at the national level) and finegrained (events at two separate border locations) information. This is especially useful for analysts in disaster management and first responder organizations who need to be able to understand both macro-level behavior and changes in the immediate vicinity, to help with planning, prevention, and mitigation. We demonstrate the capabilities of our framework in uncovering previously hidden connections and explanations by comparing independent models of the border locations with their fused model to identify emergent behaviors not found in either independent location models nor in a simple linear combination of those models.

The StunRay® XL-2000 is a high-intensity hand-held portable spotlight. It produces an intensely focused (collimated) beam of incoherent optical radiation, with a spectrum that has both visible and invisible near-infrared wavelengths. It can immobilize suspects for minutes, without causing permanent loss of vision or other physical harm.

StunRay® XL-2000 provides a first-responder with a non-lethal weapon that has a continuously variable range of force options, so that the responder has a true choice between shoot and don’t shoot during an encounter with an adversary. The handheld StunRay® XL-2000 provides measured response capability allowing military, law enforcement and security personnel the ability to:

1. “Observe”: Identify potentially violent intent at distances not previously possible (<5000ft.)
2. “Warn”: Provide a visual warning to groups or individuals (600-1000 ft.)
3. “Distract”: Provide a deterrent to aggressive behavior (150-600ft.)
4. “Suppress/Incapacitate”: Completely suppress vision and incapacitate (25-150ft.)

This paper gives a short description of a low cost communication device. The wireless intercom devices presented here enable communication between any two of these products through specialized synchronization options. This communication system consists of two functionally linked radiofrequency intercoms in the 2.4GHz free band. Each of these devices consist of a hands-free cellular system containing a microphone and a loudspeaker, which is connected individually to each communication device through a plug of four (4) contacts and three point five (3.5) mm in diameter, it also has a single USB connector for battery charging (without data bus), which is made of a lithium polymer, and finally three (3) buttons from where certain functions are controlled: volume level, audio filters, on, off, among other.

Zinc oxide (ZnO) is a unique wide bandgap biocompatible material system exhibiting both semiconducting and piezoelectric properties that has a diverse group of growth morphologies. Bulk ZnO has a bandgap of 3.37 eV that corresponds to emissions in the ultraviolet (UV) spectral band. Highly ordered vertical arrays of ZnO nanowires (NWs) have been grown on substrates including silicon, SiO₂, GaN, and sapphire using a metal organic chemical vapor deposition (MOCVD) growth process. The structural and optical properties of the grown vertically aligned ZnO NW arrays were characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), and photoluminescence (PL) measurements. Compared to conventional UV sensors, detectors based on ZnO NWs offer high UV sensitivity and low visible sensitivity, and are expected to exhibit low noise, high quantum efficiency, extended lifetimes, and have low power requirements. The photoresponse switching properties of NW array based sensing devices have been measured with intermittent exposure to UV radiation, where the devices were found to switch between low and high conductivity states at time intervals on the order of a few seconds. Furthermore, NW based UV sensors and focal plane arrays (FPAs) show promise for imaging in the near marine boundary layer, an area extending up to about six meters above the ocean surface characterized by a relatively high degree of aerosols and turbulence. Envisioned applications for such sensors/FPAs potentially integrated into submarine photonic masts (which would maintain their effectiveness even in bright daylight conditions) include threat detection and threat warning.

Currently there are extensive modeling and measurement capabilities for the region extending from 100 ft above sea surface to space, but few such capabilities exist for the region extending up to 10 ft above the sea surface. By measuring and characterizing conditions in the marine boundary layer existing below 30 ft above the sea surface such as turbulence and extinction, the optical communication capabilities of maritime vessels when operating at or near the surface may be extended and enhanced. Key physical parameters such as absorption, scattering, and turbulence strength (C_{n 2}) along the propagation path have a degree of variability on meteorological conditions as well optical wavelength. Modeling of the atmospheric environment is thus critical in order to generate a good understanding of optical propagation through the atmosphere. NUWC is utilizing software provided by MZA to model Cn 2 and resultant beam propagation characteristics through the near-marine boundary layer. We are developing the capability of near-marine boundary layer atmospheric and turbulence measurements and modeling as well as optical laser link testing at outdoor test sites. Measurements are performed with optical laser links (e.g., bit rate error), scintillometer, and particle image velocimetry (PIV) cameras, while turbulence and propagation modeling is achieved using MODTRAN5, ATMTools, NSLOT, LEEDR, and WaveTrain modeling and simulation code. By better understanding the effects of turbulence on optical transmission in the near-marine boundary layer through modeling and experimental measurements, measures can be implemented to reduce the bit error rate and increase data throughput, enabling more efficient and accurate communication link capabilities.

An uncooled mid-wave infrared (MWIR) detector is developed by doping n-type 4H-SiC with Ga using a laser doping technique. Crystalline silicon carbide (SiC) is a wide bandgap covalent semiconductor material with excellent thermomechanical and optical properties. While the covalent bonding between the Si and C atoms allows n-type or p-type doping by incorporating dopant atoms into both the Si and C sites, the wide bandgap enables fabrication of optical detectors over a wide range of wavelengths. Doping SiC with Ga creates an acceptor energy level of 0.30 eV, corresponding to the MWIR wavelength of 4.21 μm. To fabricate the MWIR detector, an n-type 4H-SiC substrate is doped with Ga using a laser doping technique. Photons of wavelength ~4.21 μm excite electrons from the valence band to the acceptor level, altering the electron density, refractive index, and therefore the reflectance of the substrate. This change in reflectance constitutes the detector optical response. To understand the dynamic response of the detector, the photoexcited electron density and lifetime in the acceptor level is theoretically analyzed. This response is experimentally measured by projecting 633 nm radiation from a laser or high power light-emitting diode (LED) array off the detector at an angle towards a CMOS camera, and examining the digital output of the captured images pixel by pixel to determine the relative intensity of the reflected radiation across the detector. Through digital image processing, a distinct difference is observed in the measured intensity of light reflected off the as-received (undoped) detector sample over infrared temperatures ranging from 100°C to 600°C compared to that of the doped sample comprising quadrants characterized by different doping concentrations, evidencing a change in reflectance from MWIR exposure and thus detector response for the Ga doped SiC detector device.