Adaptive Genetic Algorithm-Aided Neural Network With Channel State Information Tensor Decomposition for Indoor Localization
提出结合反向传播神经网络、自适应遗传算法和信道状态信息张量分解的室内Wi-Fi指纹定位方法，通过张量分解降噪、小波提取特征，再用遗传算法优化神经网络权重，提高定位精度和数据处理能力。
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.