Indoor Positioning Method Based on TOA and AOA Algorithms

doi:10.12068/j.issn.1005-3026.2024.08.007

Abstract

Abstract:

To solve the problems of high cost， poor stability， and difficulty in deployment， a new indoor positioning system combining time of arrival （TOA） and angle of arrival （AOA） is proposed. The system is composed of the positioning base station and the controlled positioning unit. It is characterized by using the ultrasonic sensor arranged in the firing type to obtain the distance characteristics between the positioning base station and the controlled positioning unit， and using the angle sensor to obtain the angle characteristics of the controlled positioning unit relative to the positioning base station. A single base station is used to realize the accurate indoor positioning process. The basic structure and principle of the system are analyzed， the positioning model is established， and the fixed point positioning accuracy and tracking positioning accuracy are experimentally verified in a certain range. The experimental results show that the system has a simple structure， easy installation and strong robustness. The maximum fixed?point positioning error within the test range is less than 5 cm， and the tracking positioning error is less than 15 cm.

Key words: indoor positioning, ultrasonic wave, AOA（angle of arrival） algorithm, TOA （time of arrival） algorithm, sensor

CLC Number:

TN 98

Li-xin GUO, Yun-peng WANG. Indoor Positioning Method Based on TOA and AOA Algorithms[J]. Journal of Northeastern University(Natural Science), 2024, 45(8): 1115-1122.

Figures/Tables 13

Fig. 1 Basic structure of indoor positioning system

Fig. 2 Main control process of indoor positioning system

Fig. 3 Secondary calibration process

Fig. 4 Simplified model of the calibration process

Fig. 5 Positioning system state model

Fig. 6 Mobile platform control model

Fig. 7 Collected time response data and angle data

Fig. 8 Original location data of test points

Fig. 9 Error of original location data and performance of each optimization algorithm

Fig. 10 Location data collected using different methods

Fig. 11 Positioning data errors of different methods

Fig. 12 Pre?set travel trajectory for the mobile platform

Fig. 13 Location data and its error mapping

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