Yu Zhengyu, Chu Liu, Shi Jiajia
School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China.
Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia.
Sensors (Basel). 2023 Jul 20;23(14):6560. doi: 10.3390/s23146560.
The conventional methods for indoor localization rely on technologies such as RADAR, ultrasonic, laser range localization, beacon technology, and others. Developers in the industry have started utilizing these localization techniques in iBeacon systems that use Bluetooth sensors to measure the object's location. The iBeacon-based system is appealing due to its low cost, ease of setup, signaling, and maintenance; however, with current technology, it is challenging to achieve high accuracy in indoor object localization or tracking. Furthermore, iBeacons' accuracy is unsatisfactory, and they are vulnerable to other radio signal interference and environmental noise. In order to address those challenges, our study focuses on the development of error modeling algorithms for signal calibration, uncertainty reduction, and interfered noise elimination. The new error modeling is developed on the Curve Fitted Kalman Filter (CFKF) algorithms. The reliability, accuracy, and feasibility of the CFKF algorithms are tested in the experiments. The results significantly show the improvement of the accuracy and precision with this novel approach for iBeacon localization.
传统的室内定位方法依赖于诸如雷达、超声波、激光测距定位、信标技术等技术。业内开发者已开始在使用蓝牙传感器来测量物体位置的iBeacon系统中运用这些定位技术。基于iBeacon的系统因其成本低、易于设置、信号传输和维护而颇具吸引力；然而，就目前的技术而言，要在室内物体定位或跟踪中实现高精度颇具挑战。此外，iBeacon的准确性不尽人意，且容易受到其他无线电信号干扰和环境噪声的影响。为应对这些挑战，我们的研究专注于开发用于信号校准、降低不确定性和消除干扰噪声的误差建模算法。新的误差建模是基于曲线拟合卡尔曼滤波器（CFKF）算法开发的。CFKF算法的可靠性、准确性和可行性在实验中得到了测试。结果显著表明，这种用于iBeacon定位的新方法提高了准确性和精度。
