Channel state information (CSI) can provide phase and amplitude of multichannel subcarrier to better describe signal propagation characteristics. Therefore, CSI has become one of the most commonly used features in indoor Wi-Fi localization. In addition, compared to the CSI geometric localization method, the CSI fingerprint localization method has the advantages of easy implementation and high accuracy. However, as the scale of the fingerprint database increases, the training cost and processing complexity of CSI fingerprints will also greatly increase. Based on this, this article proposes to combine backpropagation neural network (BPNN) and adaptive genetic algorithm (AGA) with CSI tensor decomposition for indoor Wi-Fi fingerprint localization. Specifically, the tensor decomposition algorithm based on the parallel factor (PARAFAC) analysis model and the alternate least squares (ALSs) iterative algorithm are combined to reduce the interference of the environment. Then, we use the tensor wavelet decomposition algorithm for feature extraction and obtain the CSI fingerprint. Finally, in order to find the optimal weights and thresholds and then obtain the estimated location coordinates, we introduce an AGA to optimize BPNN. The experimental results show that the proposed algorithm has high localization accuracy, while improving the data processing ability and fitting the nonlinear relationship between CSI location fingerprints and location coordinates.

中文翻译：

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具有通道状态信息张量分解的自适应遗传算法辅助神经网络室内定位

信道状态信息（CSI）可以提供多信道子载波的相位和幅度，以更好地描述信号传播特性。因此，CSI成为室内Wi-Fi定位中最常用的特征之一。此外，与CSI几何定位方法相比，CSI指纹定位方法具有易于实现和精度高等优点。但是，随着指纹数据库规模的增加，CSI指纹的训练成本和处理复杂度也会大大增加。基于此，本文提出将反向传播神经网络（BPNN）和自适应遗传算法（AGA）与CSI张量分解相结合进行室内Wi-Fi指纹定位。具体来说，基于并行因子（PARAFAC）分析模型的张量分解算法和交替最小二乘法（ALSs）迭代算法相结合，减少环境的干扰。然后，我们使用张量小波分解算法进行特征提取并获得CSI指纹。最后，为了找到最佳的权重和阈值，然后获得估计的位置坐标，我们引入了 AGA 来优化 BPNN。实验结果表明，该算法具有较高的定位精度，同时提高了数据处理能力，拟合了CSI位置指纹与位置坐标之间的非线性关系。我们使用张量小波分解算法进行特征提取并获得CSI指纹。最后，为了找到最佳的权重和阈值，然后获得估计的位置坐标，我们引入了 AGA 来优化 BPNN。实验结果表明，该算法具有较高的定位精度，同时提高了数据处理能力，拟合了CSI位置指纹与位置坐标之间的非线性关系。我们使用张量小波分解算法进行特征提取并获得CSI指纹。最后，为了找到最佳的权重和阈值，然后获得估计的位置坐标，我们引入了 AGA 来优化 BPNN。实验结果表明，该算法具有较高的定位精度，同时提高了数据处理能力，拟合了CSI位置指纹与位置坐标之间的非线性关系。