Department of Mechatronics and Automation, Faculty of Engineering, University of Szeged, Moszkvai krt. 9, 6725 Szeged, Hungary.
Doctoral School of Applied Informatics and Applied Mathematics, Óbuda University, Bécsi str. 96/b, 1034 Budapest, Hungary.
Sensors (Basel). 2023 Feb 7;23(4):1855. doi: 10.3390/s23041855.
Received signal strength indicator (RSSI)-based fingerprinting is a widely used technique for indoor localization, but these methods suffer from high error rates due to various reflections, interferences, and noises. The use of disturbances in the magnetic field in indoor localization methods has gained increasing attention in recent years, since this technology provides stable measurements with low random fluctuations. In this paper, a novel fingerprinting-based indoor 2D positioning method, which utilizes the fusion of RSSI and magnetometer measurements, is proposed for mobile robots. The method applies multilayer perceptron (MLP) feedforward neural networks to determine the 2D position, based on both the magnetometer data and the RSSI values measured between the mobile unit and anchor nodes. The magnetic field strength is measured on the mobile node, and it provides information about the disturbance levels in the given position. The proposed method is validated using data collected in two realistic indoor scenarios with multiple static objects. The magnetic field measurements are examined in three different combinations, i.e., the measurements of the three sensor axes are tested together, the magnetic field magnitude is used alone, and the Z-axis-based measurements are used together with the magnitude in the X-Y plane. The obtained results show that significant improvement can be achieved by fusing the two data types in scenarios where the magnetic field has high variance. The achieved results show that the improvement can be above 35% compared to results obtained by utilizing only RSSI or magnetic sensor data.
接收信号强度指示（RSSI）指纹识别是一种广泛应用于室内定位的技术，但由于各种反射、干扰和噪声的存在，这些方法的误差率较高。近年来，磁场中的干扰在室内定位方法中的应用受到了越来越多的关注，因为这项技术提供了稳定的测量结果，随机波动较小。本文提出了一种基于 RSSI 和磁力计测量值融合的新型指纹识别室内 2D 定位方法，用于移动机器人。该方法应用多层感知器（MLP）前馈神经网络，根据磁力计数据和移动单元与锚节点之间测量的 RSSI 值，确定 2D 位置。在移动节点上测量磁场强度，提供给定位置的干扰水平信息。该方法在具有多个静态物体的两个真实室内场景中收集的数据进行了验证。在三种不同的组合中检查了磁场测量值，即一起测试三个传感器轴的测量值，单独使用磁场强度，以及在 X-Y 平面中与幅度一起使用 Z 轴测量值。所得结果表明，在磁场变化较大的情况下，融合两种数据类型可以显著提高性能。与仅使用 RSSI 或磁力计数据获得的结果相比，所获得的结果表明，改进可以超过 35%。
