Soro Bedionita, Lee Chaewoo
Department of Electrical and Computer Engineering, Ajou University, Suwon 16499, Korea.
Sensors (Basel). 2019 Apr 14;19(8):1790. doi: 10.3390/s19081790.
The performance of an Artificial Neural Network (ANN)-based algorithm is subject to the way the feature data is extracted. This is a common issue when applying the ANN to indoor fingerprinting-based localization where the signal is unstable. To date, there is not adequate feature extraction method that can significantly mitigate the influence of the receiver signal strength indicator (RSSI) variation that degrades the performance of the ANN-based indoor fingerprinting algorithm. In this work, a wavelet scattering transform is used to extract reliable features that are stable to small deformation and rotation invariant. The extracted features are used by a deep neural network (DNN) model to predict the location. The zero and the first layer of decomposition coefficients were used as features data by concatenating different scattering path coefficients. The proposed algorithm has been validated on real measurements and has achieved good performance. The experimentation results demonstrate that the proposed feature extraction method is stable to the RSSI variation.
基于人工神经网络（ANN）的算法性能取决于特征数据的提取方式。在将人工神经网络应用于基于室内指纹定位且信号不稳定的情况时，这是一个常见问题。迄今为止，尚无足够的特征提取方法能够显著减轻接收信号强度指示符（RSSI）变化的影响，而这种变化会降低基于人工神经网络的室内指纹算法的性能。在这项工作中，使用小波散射变换来提取对小变形稳定且具有旋转不变性的可靠特征。深度神经网络（DNN）模型使用提取的特征来预测位置。通过连接不同的散射路径系数，将分解系数的零层和第一层用作特征数据。所提出的算法已在实际测量中得到验证，并取得了良好的性能。实验结果表明，所提出的特征提取方法对RSSI变化具有稳定性。
