Automatic Target recognition (ATR) seeks to improve upon techniques from signal processing, patternrecognition (PR), and information fusion. Currently, there is interest to extend traditional ATR methods by employing...
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ISBN:
(纸本)9781510626423
Automatic Target recognition (ATR) seeks to improve upon techniques from signal processing, patternrecognition (PR), and information fusion. Currently, there is interest to extend traditional ATR methods by employing Artificial Intelligence (AI) and Machine Learning (ML). In support of current opportunities, the paper discusses a methodology entitled: Systems Experimentation efficiency effectives Evaluation Networks (SEeeEN). ATR differs from PR in that ATR is a system deployment leveraging patternrecognition (PR) in a networked environment for mission decision making, while PR/ML is a statistical representation of patterns for classification. ATR analysis has long been part of the COMPrehensive Assessment of Sensor Exploitation (COMPASE) Center utilizing measures of performance (e.g., efficiency) and measures of effectiveness (e.g., robustness) for ATR evaluation. The paper highlights available multimodal data sets for Automated ML Target recognition (AMLTR).
Wide Area Motion Imagery (WAMI) systems used on surveillance aircraft may suffer from system calibration errors associated with frequent re-installation. These geo-coding errors corrupt the quality of mapping of the t...
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ISBN:
(数字)9781510618107
ISBN:
(纸本)9781510618107
Wide Area Motion Imagery (WAMI) systems used on surveillance aircraft may suffer from system calibration errors associated with frequent re-installation. These geo-coding errors corrupt the quality of mapping of the tracked objects, 'movers', from the image frame into world reference frame. In this study, an automated system for calibration of the imagery captured with six-camera WAMI array has been developed. The automatic calibration was achieved by a system of several multi-scale feature classifiers adaptively applied to an image captured by the camera array dependent on the feature availability and classifier accuracy. The feature extraction and association modules were designed to be operating independently on a frame from any given camera. The choice of the module was performed automatically using a decision tree designed as a part of the system architecture. Calculation of the per-frame corrections to mitigate the localisation error of the movers was performed by associating the features detected in each individual camera and features extracted from available satellite imagery used as a datum. The effects of the distance to the feature and the choice of the feature extraction module on the mover localisation accuracy have been evaluated on 300 frames (6 images each) captured with the WAMI array. Significant reduction in the magnitude of the geo-coding error (from 15.77-36.54 m to 5.42-8.55 m on average) was achieved and can be seen in improved alignment of the features projected into the frame as well as the reliable mapping of the mover trajectories across frames. Unlike similar systems, focusing on post-processing, the WAMI calibration system presented in the paper was designed for continuous parameter estimation in real-time.
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