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Title: Data association in multi-target tracking: a solution using a layered Boltzmann machine
Authors: Ronald A. Iltis
Pei-Yih Ting
Contributors: 國立臺灣海洋大學:資訊工程學系
NTOU:Department of Computer Science and Engineering
Keywords: Neurons;Digital audio players;Target tracking;Concurrent computing;Electric variables measurement;Frequency;Fasteners;Information processing;Hopfield neural networks;Aerospace control
Date: 1991-07
Issue Date: 2018-05-07T07:46:56Z
Publisher: Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on
Abstract: Abstract:
Data association is the problem of determining the origin of measurements in a multitarget tracking algorithm, and assigning probabilities beta /sub i//sup t/ to the event that the i-th measurement originated from the t-th target. A parallel computational method is presented for solving the data association problem using a layered Boltzmann machine. The association probabilities can be computed with arbitrarily small errors if a sufficient number of layers of binary neurons are available. Specifically, the probability beta /sub i//sup j/ is shown to be equal to the relative frequency of activation of neuron v(i,j) in a layered two-dimensional network. The authors present some simple tracking examples comparing the performance of the Boltzmann algorithm to the exact data association solution, and with the performance of an alternative parallel method using the Hopfield neural network.
Relation: pp. I31-I36
Appears in Collections:[Department of Computer Science and Engineering] Lecture & Seminar

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