2005
DOI: 10.1109/tro.2005.851375
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Using PCA in time-of-flight vectors for reflector recognition and 3-D localization

Abstract: This paper presents a reflector recognition and localization technique in three-dimensional (3-D) environments, using only times-of-flight (TOFs) data obtained from ultrasonic transducers. The recognition and localization technique is based on the principal component analysis applied to the TOF vectors originating from a sensor that contains two emitting transducers and several receivers. The two emitters simultaneously transmit two coded pulses that are detected later on and discriminated by the receivers, af… Show more

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Cited by 38 publications
(19 citation statements)
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References 29 publications
(41 reference statements)
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“…The same features characterize the method presented in [14], which extends the approach described in [8] to the 3-D case. Analogous features for the 3-D case can also be found in the methods presented in [7,16]. The accuracy of azimuth angle determination, reported in descriptions of the mentioned methods, is usually 1…”
Section: Related Workmentioning
confidence: 87%
“…The same features characterize the method presented in [14], which extends the approach described in [8] to the 3-D case. Analogous features for the 3-D case can also be found in the methods presented in [7,16]. The accuracy of azimuth angle determination, reported in descriptions of the mentioned methods, is usually 1…”
Section: Related Workmentioning
confidence: 87%
“…This kind of system was also applied by Li & Kleeman, (1995) to differentiate walls and corners. In Jimenez et al, (2005) a classification method based on the principal component-analysis technique was proposed. A sonar system which was used in implementation of the method consisted of eight ultrasonic transducers.…”
Section: Ultrasonic Sensingmentioning
confidence: 99%
“…There are many applications where matrices are real and symmetric, with sizes not exceeding 20 × 20 [5,6]. In this situation, EVD computation is easier, and there are many algorithms that take advantage of this.…”
Section: Introductionmentioning
confidence: 99%