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Multiple Target Tracking Using KNN-Based Classifier and Probabilistic Data Association
, T.C. Chaitra, Y. Gupta
Published in Springer Science and Business Media Deutschland GmbH
Volume: 700
Pages: 271 - 281
One of the major problems in target tracking is to identify the measurement of the interested target among the bulk of data. Many algorithms based on identifying single measurement among the received data were presented. However with increase in false alarm rate these models failed. Instead of identifying single measurement among the received signals and discarding others, alternative approach is to associate each measurement with different weights and this approach is called Probabilistic Data association (PDA). However performance of PDA algorithm is visibly degraded for multiple targets. Hence this paper presents the integration of PDA with K-nearest neighbors’ algorithm (KNN) to effectively track multiple targets and reduce the complexity in multiple target tracking. KNN classifies the measurements among targets and then PDA algorithm is applied to each target. Hence the multiple targets tracking problem is split into multiple- single target tracking problem with the help of KNN. Simulations were done using MATLAB and their performances are presented in this paper. © 2021, Springer Nature Singapore Pte Ltd.
About the journal
JournalData powered by TypesetLecture Notes in Electrical Engineering
PublisherData powered by TypesetSpringer Science and Business Media Deutschland GmbH