Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, Università degli Studi di Napoli Federico II, 80125 Naples, Italy.
Dipartimento di Ingegneria Industriale, Università degli Studi di Napoli Federico II, 80125 Naples, Italy.
Sensors (Basel). 2022 Dec 8;22(24):9615. doi: 10.3390/s22249615.
In the automotive field, the introduction of keyless access systems is revolutionizing car entry techniques currently dominated by a physical key. In this context, this paper investigates the possible use of smartphones to create a PEPS (Passive Entry Passive Start) system using the BLE (Bluetooth Low-Energy) Fingerprinting technique that allows, along with a connection to a low-cost BLE micro-controllers network, determining the driver's position, either inside or outside the vehicle. Several issues have been taken into account to assure the reliability of the proposal; in particular, (i) spatial orientation of each microcontroller-based BLE node which ensures the best performance at 180° and 90° referred to as the BLE scanner and the advertiser, respectively; (ii) data filtering techniques based on Kalman Filter; and (iii) definition of new network topology, resulting from the merger of two standard network topologies. Particular attention has been paid to the selection of the appropriate measurement method capable of assuring the most reliable positioning results by means of the adoption of only six embedded BLE devices. This way, the global accuracy of the system reaches 98.5%, while minimum and maximum accuracy values relative to the individual zones equal, respectively, to 97.3% and 99.4% have been observed, thus confirming the capability of the proposed method of recognizing whether the driver is inside or outside the vehicle.
在汽车领域，无钥匙进入系统的引入正在彻底改变目前由物理钥匙主导的汽车进入技术。在这种情况下，本文研究了使用智能手机创建基于 BLE（低功耗蓝牙）指纹技术的 PEPS（被动进入，被动启动）系统的可能性，该技术允许通过连接低成本 BLE 微控制器网络，确定驾驶员在车内或车外的位置。为了确保该方案的可靠性，已经考虑了几个问题；特别是（i）基于 BLE 的每个微控制器节点的空间方位，分别确保了在 180°和 90°下的最佳性能，这被称为 BLE 扫描仪和广告商；（ii）基于卡尔曼滤波器的数据滤波技术；以及（iii）定义了新的网络拓扑结构，这是两种标准网络拓扑结构合并的结果。特别注意选择适当的测量方法，通过采用仅六个嵌入式 BLE 设备，确保最可靠的定位结果。这样，系统的整体精度达到 98.5%，而各个区域的最小和最大精度值分别为 97.3%和 99.4%，从而证实了所提出的方法能够识别驾驶员是否在车内或车外的能力。
