Method for considering angle error in the position estimation of a moving target using ultrasonic array

Hattori, Masamichi; Tsujii, Asuka; Kasashima, Takashi; Hatano, Hiroyuki; Yamazato, Takaya

doi:10.1587/comex.2021xbl0088

Cited by 4 publications

(2 citation statements)

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“…Ultrasonic sensors are widely used for vehicles such as parking assistance and industrial applications such as robotics because of their simple structure, low cost, and excellent environmental resistance [1], [2]. We can extend the detection range and estimate the positions of obstacles using an array of ultrasonic sensors [3][4][5][6][7]. It is expected to be used in low-speed autonomous vehicles.…”

Section: Introductionmentioning

confidence: 99%

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MAP estimation using two temporally consecutive data for ultrasonic sensor arrays

Hattori

Tsujii

Kasashima³

et al. 2023

IEICE ComEX

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This paper proposes MAP estimation using two temporally consecutive data using signals from eight ultrasonic sensors forming a linear array. First, we estimate the distance to the obstacle from the time difference between the eight received signals. Then, assuming that the ranging error follows a Gaussian distribution and that each of the eight ranging values is independent, we can get the existence probability of the obstacle's position by a pair of two obtained distances. Finally, we estimate the position of the obstacle by multiplying 28 ( 8 C 2 ) existence probabilities obtained. The conventional method estimates the position of an obstacle by the above procedure. However, the estimation accuracy in the angular direction was poor, resulting in the spread of the existence probability in the horizontal direction. In the proposed MAP estimation using two temporally consecutive data, we obtain the existence probability of an obstacle by the procedure shown above, and then use it as a prior probability of the obstacle's estimated position. Furthermore, we recursively perform the same process to obtain the existence probability of the obstacle. In this way, we improve the accuracy of the estimation of the position of the obstacle by the existence probability. We present the experiment results to show the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

“…It is expected to be used in low-speed autonomous vehicles. However, the precision of the estimation of the position of the obstacle, especially in the angular direction, is poor, resulting in a wide distribution of the probability of the existence of the obstacle [3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%