MUltiSensor Target Recognition System (MUSTRS)

Forman, Arthur V.; Brown, David B.; Hughen, James H.; Pressley, R.R.; Sanders, Albert R.; Sullivan, Daniel J.

doi:10.1109/acssc.1993.342514

Cited by 3 publications

(2 citation statements)

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“…Target recognition is to confirm the category and identity of an unknown target according to its feature parameters. Due to the uncertainties in feature values, most of target recognition methods were developed typically based on the uncertainty theory, for example, D-S evidence [1,2], Bayesian inference [3,4], attribute measure [5], and fuzzy set [6]. However, these methods describe uncertainty information with drawbacks that resulted in the errors.…”

Section: Introductionmentioning

confidence: 99%

Algorithm for Target Recognition Based on Interval-Valued Intuitionistic Fuzzy Sets with Grey Correlation

Huang¹,

Guo

Li³

et al. 2016

Mathematical Problems in Engineering

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In order to improve exact recognition ratios for aerial targets, this paper presents a novel algorithm for target recognition based on interval-valued intuitionistic fuzzy sets with grey correlation. Drawbacks of some previously proposed methods are analyzed, and then a novel algorithm is presented. Recognition matrix of an aerial target is established first. Every entry associated with the matrix is an interval-valued intuitionistic fuzzy number, which is composed of interval-valued membership and nonmembership, representing the relation of the target to one category in terms of one characteristic parameter. Then grey correlation theory is used to analyze the recognition matrix to obtain the grey correlation degree of this unknown target to every category. 200 sets of target recognition data are used to compare the proposed algorithm with traditional methods. Experimental results verify that the correct recognition ratio can be up to 99.5% that satisfies the expectations, which shows the proposed algorithm can solve the target recognition problems better. The proposed algorithm can be used to solve the uncertain inference problems, such as target recognition, threat assessment, and decision making.

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Section: Introductionmentioning

confidence: 99%

Algorithm for Target Recognition Based on Interval-Valued Intuitionistic Fuzzy Sets with Grey Correlation

Huang¹,

Guo

Li³

et al. 2016

Mathematical Problems in Engineering

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“…One of the most intriguing properties of a neural network is the ability to dynamically learn the interconnection weights that correspond to a desired behavior of the network nonlinear processing units. [3][4][5][6][7][8][9] The innate parallel processing and unique 3-D geometric capability of optics complements rather than competes with electronic systems by addressing problem domains that tax the capabilities of conventional electronic processors. [10][11][12] Because the parallel processing capability of optics permits these operations to take place at high speed, 2-D optical processors are well suited for these problems.…”

Section: Introductionmentioning

confidence: 99%

Compact holographic optical neural network system for real‐time pattern recognition

Mintzer

Kostrzewski

et al. 1996

Opt. Eng

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One of the important characteristics of artificial neural networks is their capability for massive interconnection and parallel processing. Recently, specialized electronic neural network processors and VLSI neural chips have been introduced in the commercial market. The number of parallel channels they can handle is limited because of the limited parallel interconnections that can be implemented with onedimensional electronic wires. High-resolution pattern recognition problems can require a large number of neurons for parallel processing of an image. This paper describes a holographic optical neural network (HONN) that is based on high-resolution volume holographic materials and is capable of performing massive 3-D parallel interconnection of tens of thousands of neurons. A HONN with more than 16,000 neurons packaged in an attache case has been developed. Rotation-shift-scaleinvariant pattern recognition operations have been demonstrated with this system. System parameters such as the signal-to-noise ratio, dynamic range, and processing speed are discussed. © 1996 Society of PhotoOptical Instrumentation Engineers.Subject terms: optical neural networks; holographic optical neural networks; invariant pattern recognition; automatic target recognition; hybrid optical neural networks; compact neural network systems.Paper

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