An integrated approach to passive target classification

Rajagopal, R.; Kumar, K. A. Varun; Rao, P. Ramakrishna

doi:10.1109/icassp.1994.389657

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…40 Use of artificial intelligence is not new in target identification and in classification, and expert systems and neural networks are used for this purpose as alternative techniques to statistical classifiers. 41 As a civil application, it is seen that fuzzy reasoning is used as an object detection method in a fire by combining image processing techniques. 42 After extracting moving targets, fuzzy membership values are calculated for each characteristic target parameters, and a knowledge base is used to classify the targets. A standard expert system implementation in target weapon assignment is proposed by Sahin and Leblebicioglu.…”

Section: Extended Literaturementioning

confidence: 99%

Agent-based target evaluation and fire doctrine: an aspect-oriented programming view

Hocaoğlu

2021

Journal of Defense Modeling & Simulation

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Threat evaluation is a vital process for any defense system, and it consists of a series of calculation and evaluation based on the inferred capabilities and intents of the targets that aim to give damage to defended assets. Target evaluation is proceeded in a wargame and the aim is to compare weapon target pairs according to a set of criteria. The target evaluation cycle is repeated anytime a new detection is received and when any change happens in the target currently detected. The whole process consists of a set of tasks that are shared between Command and Control units and the tasks require different responsibilities. Each task is succeeded by a specific behavior that is represented as a reasonably ordered set of actions. The task sharing is organized by taking the C2 architectures into account. In this paper, an agent-based command and control entity, which is in charge of target evaluation and giving engagement decision, is designed and it is situated in an air defense simulation environment. The study aims to propose an agent design in military decision-making domain, bringing analytic methods with the first-order logic together, and combine aspect orientation with agent design. The study also improves dynamic aspect management in agent programming using the relation concept.

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Section: Extended Literaturementioning

confidence: 99%

Agent-based target evaluation and fire doctrine: an aspect-oriented programming view

Hocaoğlu

2021

Journal of Defense Modeling & Simulation

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“…Upper and lower bounds on the Bayes error rate exist that are minimized by maximizing MI [3], [4], [5]. However, MI is not in common use since the estimation of Shannon's entropy is computationally intensive [6] (estimation of Shannon's entropy can be accomplished by discretizing the variables [6], [7], [8], [9], [10] or by using an O N T 2 nonparametric estimator [11]).…”

Section: Information-theoretic Feature Extractionmentioning

confidence: 99%

Feature extraction using information-theoretic learning

Hild

Erdoğmuş

Torkkola

et al. 2006

IEEE Trans. Pattern Anal. Mach. Intell.

135

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A classification system typically consists of both a feature extractor (preprocessor) and a classifier. These two components can be trained either independently or simultaneously. The former option has an implementation advantage since the extractor need only be trained once for use with any classifier, whereas the latter has an advantage since it can be used to minimize classification error directly. Certain criteria, such as Minimum Classification Error, are better suited for simultaneous training, whereas other criteria, such as Mutual Information, are amenable for training the feature extractor either independently or simultaneously. Herein, an information-theoretic criterion is introduced and is evaluated for training the extractor independently of the classifier. The proposed method uses nonparametric estimation of Renyi's entropy to train the extractor by maximizing an approximation of the mutual information between the class labels and the output of the feature extractor. The evaluations show that the proposed method, even though it uses independent training, performs at least as well as three feature extraction methods that train the extractor and classifier simultaneously.

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“…The effectiveness pertains to the uniqueness of the feature with respect to the designated class in spite of the environmental degradation. Many efforts have been made in the past to classify marine vessels based on their spectral characteristics [12][13][14][15][16][17][18][19][20][21][22][23] and more recently on the basis of the fractal features [24,25], chaotic features [26] and eigenmode decomposition [27]. The previously available literature does not explicitly attempt to mitigate the severe underwater channel distortions.…”

Section: Introductionmentioning

confidence: 99%