提出了一种基于拉依达准则-相关系数-卷积神经网络(Pauta criterion-correlation coefficient-convolutional neural networks,P-C-CNN)的时间序列融合定位算法.P-C-CNN方法整合了不同节点以及不同时间序列的数据点,利用时间和空间数据的相互关联性,提高了室内定位的精度和可靠性.首先,该方法使用拉依达准则-相关系数(Pauta criterion-correlation coefficient,P-C)算法对到达角度(Angle of arrival,AOA)-接收信号强度(Received signal strength,RSS)数据的异常值进行剔除,提高了训练数据的质量.其次,算法对数据进行随机间隔选取,从而缩短模型训练时间,同时较好地模拟在线定位阶段数据选取的不确定性,减少模型对训练数据的过度拟合.再次,传统单帧信息训练方法由于噪声混杂无法稳定提取信息特征,所提算法在连续采集的时间序列数据中,融合随机选取固定长度的多帧AOA-RSS数据,然后利用卷积神经网络(Convolutional neural networks,CNN)进行特征提取,避免了单帧信号定位中误差波动较大的问题.最后,通过大量实际测试,验证了所提方法的有效性.实验结果表明,在典型室内环境中,与仅采用RSS数据或者AOA信息的指纹定位算法相比,本文算法的分类准确率由 91.6%提高到了 96.4%,定位精度从 1.3 m提高到了 0.3 m;与传统基于模型的AOA-RSS联合定位相比,本文算法能较好解决实测中多径效应等干扰因素的影响,定位精度从1.1 m提高到了0.3 m.
Indoor Positioning Based on Time Sequences Fusion
This paper proposes a novel indoor positioning algorithm based on time sequences fusion in Pauta criterion-correlation coefficient convolutional neural networks(P-C-CNN).The P-C-CNN approach integrates data points from different nodes and various time sequences,leveraging the interconnectedness of temporal and spatial data to enhance the accuracy and reliability of indoor positioning.Firstly,this method utilizes the Pauta criterion-correlation coefficient(P-C)algorithm to remove outliers in angle of arrival(AOA)-received signal strength(RSS)data,improving the quality of the training data.Secondly,the algorithm randomly selects data at intervals,reducing the training time of the model and effectively simulating the uncertainty of data selection in the online positioning phase,thus reducing overfitting of the model to the training data.Furthermore,the traditional single-frame information training method is unable to stably extract information features due to the mixture of noise.The proposed algorithm randomly selects multiple frames of fixed length from the continuously collected AOA-RSS data within time sequences fusion,and then employs convolutional neural networks(CNN)for feature extraction.This approach can avoid the issue of large error fluctuations commonly encountered in single-frame signal positioning.Finally,through extensive practical testing,this paper has validated the effectiveness of the proposed method.The experimental results demonstrate that in typical indoor environments,compared to fingerprint positioning algorithms that solely rely on RSS data or AOA information,the proposed algorithm achieves an improved classification accuracy from 91.6%to 96.4%,and the positioning accuracy is improved from 1.3 m to 0.3 m.Moreover,compared to the traditional model-based AOA-RSS joint positioning,this algorithm effectively addresses the influence of interference factors such as multipath effects observed in real-world measurements.The positioning accuracy is improved from 1.1 m to 0.3 m.
万方数据
维普
