86 research outputs found

    Optimization of Automatic Target Recognition with a Reject Option Using Fusion and Correlated Sensor Data

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    This dissertation examines the optimization of automatic target recognition (ATR) systems when a rejection option is included. First, a comprehensive review of the literature inclusive of ATR assessment, fusion, correlated sensor data, and classifier rejection is presented. An optimization framework for the fusion of multiple sensors is then developed. This framework identifies preferred fusion rules and sensors along with rejection and receiver operating characteristic (ROC) curve thresholds without the use of explicit misclassification costs as required by a Bayes\u27 loss function. This optimization framework is the first to integrate both vertical warfighter output label analysis and horizontal engineering confusion matrix analysis. In addition, optimization is performed for the true positive rate, which incorporates the time required by classification systems. The mathematical programming framework is used to assess different fusion methods and to characterize correlation effects both within and across sensors. A synthetic classifier fusion-testing environment is developed by controlling the correlation levels of generated multivariate Gaussian data. This synthetic environment is used to demonstrate the utility of the optimization framework and to assess the performance of fusion algorithms as correlation varies. The mathematical programming framework is then applied to collected radar data. This radar fusion experiment optimizes Boolean and neural network fusion rules across four levels of sensor correlation. Comparisons are presented for the maximum true positive rate and the percentage of feasible thresholds to assess system robustness. Empirical evidence suggests ATR performance may improve by reducing the correlation within and across polarimetric radar sensors. Sensitivity analysis shows ATR performance is affected by the number of forced looks, prior probabilities, the maximum allowable rejection level, and the acceptable error rates

    Combat Identification with Sequential Observations, Rejection Option, and Out-of-Library Targets

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    This research extends a mathematical framework to select the optimal sensor ensemble and fusion method across multiple decision thresholds subject to warfighter constraints for a combat identification (CID) system. The formulation includes treatment of exemplars from target classes on which the CID system classifiers are not trained (out-of-library classes) and enables the warfighter to optimize a CID system without explicit enumeration of classifier error costs. A time-series classifier design methodology is developed and applied, yielding a multi-variate Gaussian hidden Markov model (HMM). The extended CID framework is used to compete the HMM-based CID system against a template-based CID system. The framework evaluates competing classifier systems that have multiple fusion methods, varied prior probabilities of targets and non-targets, varied correlation between multiple sensor looks, and varied levels of target pose estimation error. Assessment using the extended framework reveals larger feasible operating regions for the HMM-based classifier across experimental settings. In some cases the HMM-based classifier yields a feasible region that is 25\% of the threshold operating space versus 1\% for the template-based classifier

    New Pose Estimation Methodology for Target Tracking and Identification

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    Ground Moving Target Indicator (GMTI) and High Resolution Radar (HRR) can track position and velocity of ground moving target. Pose, angle between position and velocity, can be derived from kinematics estimates of position and velocity and it is often used to reduce the search space of a target identification (ID) and Automatic Target Recognition (ATR)  algorithms. Due to low resolution in some radar systems, the GMTI estimated pose may exhibit large errors contributing to a faulty identification of potential targets. Our goal is to define new methodology to improve pose estimate. Besides applications in target tracking, there are numerous commercial applications in machine learning, augmented reality and body tracking

    A Hybrid Templated-Based Composite Classification System

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    An automatic target classification system contains a classifier which reads a feature as an input and outputs a class label. Typically, the feature is a vector of real numbers. Other features can be non-numeric, such as a string of symbols or alphabets. One method of improving the performance of an automatic classification system is through combining two or more independent classifiers that are complementary in nature. Complementary classifiers are observed by finding an optimal method for partitioning the problem space. For example, the individual classifiers may operate to identify specific objects. Another method may be to use classifiers that operate on different features. We propose a design for a hybrid composite classification system, which exploits both real-numbered and non-numeric features with a template matching classification scheme. This composite classification system is made up of two independent classification systems.These two independent classification systems, which receive input from two separate sensors are then combined over various fusion methods for the purpose of target identification. By using these two separate classifiers, we explore conditions that allow the two techniques to be complementary in nature, thus improving the overall performance of the classification system. We examine various fusion techniques, in search of the technique that generates the best results. We investigate different parameter spaces and fusion rules on example problems to demonstrate our classification system. Our examples consider various application areas to help further demonstrate the utility of our classifier. Optimal classifier performance is obtained using a mathematical framework, which takes into account decision variables based on decision-maker preferences and/or engineering specifications, depending upon the classification problem at hand

    Ground target classification for airborne bistatic radar

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    Automatic Target Recognition for Hyperspectral Imagery

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    Automatic target detection and recognition in hyperspectral imagery offer passive means to detect and identify anomalies based on their material composition. In many combat identification approaches through pattern recognition, a minimum level of confidence is expected with costs associated with labeling anomalies as targets, non-targets or out-of-library. This research approaches the problem by developing a baseline, autonomous four step automatic target recognition (ATR) process: 1) anomaly detection, 2) spectral matching, 3) out-of-library decision, and 4) non-declaration decision. Atmospheric compensation techniques are employed in the initial steps to compare truth library signatures and sensor processed signatures. ATR performance is assessed and additionally contrasted to two modified ATRs to study the effects of including steps three and four. Also explored is the impact on the ATR with two different anomaly detection methods

    Ballistic Flash Characterization: Penetration and Back-face Flash

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    The Air Force is extremely concerned with the safety of its people, especially those who are flying aircraft. Aircrew members flying combat missions are concerned with the chance that a fragment from an exploding threat device may penetrate into the airframe to possibly ignite a fire onboard the aircraft. One concern for vulnerability revolves around a flash that may occur when a projectile strikes and penetrates an aircraft\u27s fuselage. When certain fired rounds strike the airframe, they break into fragments called spall. Spall and other fragmentation from an impact often gain enough thermal energy to oxidize the materials involved. This oxidation causes a flash. To help negate these incidents, analysts must be able to predict the flash that can occur when a projectile strikes an aircraft. This research directly continues AFIT work for the 46th Test Group, Survivability Analysis Flight, by examining models to predict the likelihood of penetration of a fragment fired at a target. Empirical live-fire fragment test data are used to create an empirical model of a flash event. The resulting model provides an initial back-face flash modeling capability that can be implemented in joint survivability analysis models

    Feedback-assisted automatic target and clutter discrimination using a Bayesian convolutional neural network for improved explainability in SAR applications

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    DATA AVAILABILITY STATEMENT : The NATO-SET 250 dataset is not publicly available; however, the MSTAR dataset can be found at the following url: https://www.sdms.afrl.af.mil/index.php?collection=mstar (accessed on 5 January 2022).In this paper, a feedback training approach for efficiently dealing with distribution shift in synthetic aperture radar target detection using a Bayesian convolutional neural network is proposed. After training the network on in-distribution data, it is tested on out-of-distribution data. Samples that are classified incorrectly with high certainty are fed back for a second round of training. This results in the reduction of false positives in the out-of-distribution dataset. False positive target detections challenge human attention, sensor resource management, and mission engagement. In these types of applications, a reduction in false positives thus often takes precedence over target detection and classification performance. The classifier is used to discriminate the targets from the clutter and to classify the target type in a single step as opposed to the traditional approach of having a sequential chain of functions for target detection and localisation before the machine learning algorithm. Another aspect of automated synthetic aperture radar detection and recognition problems addressed here is the fact that human users of the output of traditional classification systems are presented with decisions made by “black box” algorithms. Consequently, the decisions are not explainable, even to an expert in the sensor domain. This paper makes use of the concept of explainable artificial intelligence via uncertainty heat maps that are overlaid onto synthetic aperture radar imagery to furnish the user with additional information about classification decisions. These uncertainty heat maps facilitate trust in the machine learning algorithm and are derived from the uncertainty estimates of the classifications from the Bayesian convolutional neural network. These uncertainty overlays further enhance the users’ ability to interpret the reasons why certain decisions were made by the algorithm. Further, it is demonstrated that feeding back the high-certainty, incorrectly classified out-of-distribution data results in an average improvement in detection performance and a reduction in uncertainty for all synthetic aperture radar images processed. Compared to the baseline method, an improvement in recall of 11.8%, and a reduction in the false positive rate of 7.08% were demonstrated using the Feedback-assisted Bayesian Convolutional Neural Network or FaBCNN.The Radar and Electronic Warfare department at the CSIR.http://www.mdpi.com/journal/remotesensinghj2023Electrical, Electronic and Computer Engineerin

    Effects of The BCR-ABLl Oncogene on DNA Damage and Repair

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    Chronic myeloid leukemia (CML) is a two-stage disease caused by the p210 BCR-ABL protein. BCR-ABL is a constitutively activated tyrosine kinase that activates signaling pathways to enhance proliferation and inhibit apoptosis. BCR-ABL kinase activity directly causes CML chronic phase, the first stage of CML, but the role of BCR-ABL in transitioning to blast crisis (BC), the second stage of disease, remains under investigation. Given that CML BC is marked by additional chromosomal abnormalities, the role of BCR-ABL in contributing to genomic instability through effects on DNA damage and DNA repair processes has been studied. We investigated what impact BCR-ABL has on the development of DNA double strand breaks (DSBs) and mis-repair in a cell system that is unable to undergo apoptosis. We determined that the failure of cells to undergo apoptosis after DNA damage leads to genomic instability, which is not further increased by BCR-ABL expression. We expressed BCR-ABL in a mouse hematopoietic cell line null for the pro-apoptotic proteins Bax and Bak (DKO). Both DKO cells and the BCR-ABL-expressing cell line (DBA) fail to undergo apoptosis after γ-irradiation (IR). DKO cells are dependent on interleukin-3 (IL-3) for growth and proliferation, but expression of BCR-ABL renders them IL-3 independent. We compared the induction of DSBs after IR and determined that unlike apoptosis-competent cell lines, which demonstrate increased DSB formation in the presence of BCR-ABL, there was no difference in DSB formation comparing DKO to DBA cells. This suggests that the Bax/Bak-mediated induction of apoptosis may be important in the DNA damage response. We also evaluated the level of misrepair in DKO and DBA cells after IR using spectral karyotype (SKY) analysis. We determined that DKO and DBA cells showed a similar accumulation of new chromosomal abnormalities, including gains and losses of chromosomes as well as translocations, after IR and repair. These results suggest that the BCR-ABL-mediated effects on DNA damage and repair pathways may occur through inhibition of apoptosis rather than or in addition to direct effects on DNA repair pathways. We postulate that BCR-ABL shifts the apoptotic threshold, allowing a group of cells with an increased amount of damage to survive
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