TY - JOUR
T1 - Improved Obstacle Mitigation and Localization Accuracy in Narrowband Ultrasonic Localization Systems using RoBCUL Algorithm
AU - Haigh, Sebastian
AU - Kulon, Janusz
AU - Partlow, Adam
AU - Rogers, Paul
AU - Gibson, Colin
N1 - Funding Information:
Manuscript received August 8, 2019; revised October 28, 2019; accepted December 15, 2019. Date of publication November 20, 2019; date of current version April 7, 2020. This work was supported in part by the Welsh Government’s ESF-funded Knowledge Economy Skill Scholarships through the Cardiff and Vale University Health Board’s Rehabilitation Engineering Unit and Computing and Digital Economy Research Institute of the University of South Wales under Grant MAXI 20422. The Associate Editor coordinating the review process was José Pereira. (Corresponding author: Sebastian Haigh.) Sebastian Haigh and Janusz Kulon are with the Faculty of Computing, Engineering and Science, University of South Wales, Pontypridd CF37 1DL, U.K. (e-mail: [email protected]).
Publisher Copyright:
© 1963-2012 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/1/6
Y1 - 2020/1/6
N2 - This article develops a method for mitigating the negative effects of obstacles in narrowband, time-division multiple access (TDMA), ultrasonic localization systems. The method builds upon the robust Bayesian classifier for ultrasonic localization (RoBCUL) algorithm that utilizes an iteratively reweighted least-squares (IRLS) scheme. This algorithm has the advantage of low computational cost but loses performance in the presence of obstacles. The improved version of the RoBCUL algorithm presented in this article uses a statistical test applied after each iteration of the regression, using a weighted-residual vector calculated from the weight matrix and residual vector. The technique was tested using experimental data with its performance being quantified by its ability to correctly classify all the signals received during a single TDMA cycle. The extended version performed significantly better in all obstacle scenarios than the original, correctly classifying 100% of TDMA cycles in the scenarios with no obstacles, 99.9% with one obstacle, and 98.7% with two obstacles.
AB - This article develops a method for mitigating the negative effects of obstacles in narrowband, time-division multiple access (TDMA), ultrasonic localization systems. The method builds upon the robust Bayesian classifier for ultrasonic localization (RoBCUL) algorithm that utilizes an iteratively reweighted least-squares (IRLS) scheme. This algorithm has the advantage of low computational cost but loses performance in the presence of obstacles. The improved version of the RoBCUL algorithm presented in this article uses a statistical test applied after each iteration of the regression, using a weighted-residual vector calculated from the weight matrix and residual vector. The technique was tested using experimental data with its performance being quantified by its ability to correctly classify all the signals received during a single TDMA cycle. The extended version performed significantly better in all obstacle scenarios than the original, correctly classifying 100% of TDMA cycles in the scenarios with no obstacles, 99.9% with one obstacle, and 98.7% with two obstacles.
KW - Bayes methods
KW - chirp modulation
KW - least squares methods
KW - position measurements
KW - ultrasonic transducers
U2 - 10.1109/TIM.2019.2963553
DO - 10.1109/TIM.2019.2963553
M3 - Article
SN - 0018-9456
VL - 69
SP - 2315
EP - 2324
JO - IEEE Transactions on Instrumentation and Measurement
JF - IEEE Transactions on Instrumentation and Measurement
IS - 5
M1 - 8950196
ER -