This paper describes the development of an information-theoretic image measure for Sensor evaluation under the contract to the United States Air Force. While current approaches are based on human perception models, a need exists for evaluation of sensors for ATC/ATR systems. Such an evaluation should be performed in terms of the probabilities of detection/identification and false alarms independent of the idiosyncrasies of the specific ATC/ATR algorithms. Such an approach based on the informationtheoretic content of images for the target vs. background separability is being developed and applied to evaluating sensors using the Tower Test data collected at the Wright Laboratories. INFORMATION THEORETIC VS. EMPIRICAL APPROACHES TO ATR SYSTEMS EVALUATIONAn ability to predict the performance of ATC/ATR algorithms and sensors is critically important for a number of applications. Tactical Decision Aid (TDA) systems need this capability for optimal settings of ATC/ATR parameters. Mission Utility Models need this capability for predicting mission success. Data collection activities need this capability for optimal experimental design. The performance of each specific ATC/ATR system depends on multiple factors such as the overall structure and concept, sensor, the performance of algorithm subcomponents, quality, adequacy of the training data set, and the accuracy of the tuning algorithm that sets parameters for specific missions. One way to model the performance of an ATC/ATR system is to obtain empirical data on the performance by using a large data set of imagery, and then, fit a (nonlinear) regression model to these performance data as a function of IM. On the other hand, it is well recognized that in each image there is specific information pertinent to the separation of target from background and to target identification, and the amount of information determines a bound on the best possible performance of any algorithm.Various applications have their own performance evaluation needs. While TDA and mission utility models should be modeling available tactical algorithms, data collection and sensor evaluation should not depend on idiosyncrasies of specific algorithms, and should be based on information-theoretic considerations. Similarly, algorithms' performances should be evaluated against information-theoretic performance bounds. In this paper we develop the Information-Theoretic Image Measure (IT IM) which utilizes the information content of an image for the purpose of sensor evaluation. Current approaches to sensor evaluation art' based on models of human operators' ability to detect and identify targets. Their results are usually expressed in terms of maximum detection/recognition range. Development o f sensors for Automatic Target Cueing and Recognition Systems (ATC/ATR) s h a1 1 be performed in terms of probabilities of detection and false alarm and shall satisfy needs of existing and future ATC/ATR systems. Characterization of sensors in terms of probabilities of detection and false alarm can be obtained by eval...
This paper describes the development of an information-theoretic image measure for sensor evaluation under the contract to the United States Air Force. While current approaches are based on human perception models, a need exists for evaluation of sensors for ATC/ATR systems. Such an evaluation should be performed in terms of the probabilities of detection/identification and false alarms independent of the idiosyncrasies of the specific ATC/ATR algorithms. Such an approach based on the information-theoretic content of images for the target vs. background separability is being developed and applied to evaluating sensors using the Tower Test data collected at the Wright Laboratories. INFORMATION THEORETIC VS. EMPIRICAL APPROACHES TO ATR SYSTEMS EVALUATIONAn ability to predict the performance of ATC/ATR algorithms and sensors is critically important for a number of applications. Tactical Decision Aid (TDA) systems need this capability for optimal settings of ATC/ATR parameters. Mission Utility Models need this capability for predicting mission success. Data collection activities need this capability for optimal experimental design. The performance of each specific ATC/ATR system depends on multiple factors such as the overall structure and concept, sensor, the performance ofalgorithm subcomponents, quality, adequacy of the training data set, and the accuracy of the tuning algorithm that sets parameters for specific missions. One way to model the performance of an ATC/ATR system is to obtain empirical data on the performance by using a large data set of imagery, and then, fit a (nonlinear) regression model to these performance data as a function of IM. On the other hand, it is well recognized that in each image there is specific information pertinent to the separation of target from background and to target identification, and the amount of information determines a bound on the best possible performance of any algorithm.Various applications have their own performance evaluation needs. While TDA and mission utility models should be modeling available tactical algorithms, data collection and sensor evaluation should not depend on idiosyncrasies of specific algorithms, and should be based on information-theoretic considerations. Similarly, algorithms' performances should be evaluated against information-theoretic performance bounds. In this paper we develop the Information-Theoretic Image Measure (IT IM) which utilizes the information content of an image for the purpose of sensor evaluation. Current approaches to sensor evaluation are based on models of human operators' ability to detect and identify targets. Their results are usually expressed in terms of maximum detection/recognition range. Development of sensors for Automatic Target Cueing and Recognition Systems (ATC/ATR) shall be performed in terms of probabilities of detection and false alarm and shall satisfy needs of existing and future ATC/ATR systems. Characterization of sensors in terms of probabilities of detection and false alarm can be obtained by evaluatin...
We describe in this paper results of a system study for an airborne lidar system that makes real-time, range-resolved measurements of clear air turbulence (CAT) to several kilometers forward of an aircraft's projected flight path. Exploiting the width of the IF signal Doppler spectrum (σf) is central to the system concept. Since broadening of the Doppler spectrum beyond the transform limit is a function of the mixture of velocities (velocity width, σv) in the pulse scattering volume, any combination of shear and/or turbulence in the flight path is immediately revealed by the broadened spectra. Hence, σf relates to a property of the velocity field that is particularly important to aircraft anti other aerospace vehicles. Explicit spectral width processing is quite powerful for CAT detection lidars because complex atmospheric wind patterns can be characterized in terms of a single observable σv for subsequent reckoning against a hazard index.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.