Although numerous schemes, including learning-based approaches, have attempted to determine a solution for location recognition in indoor environments using RSSI, they suffer from the severe instability of RSSI. Compared with the solutions obtained by recurrent-approached neural networks, various state-of-the-art solutions have been obtained using the convolutional neural network (CNN) approach based on feature extraction considering indoor conditions. Complying with such a stream, this study presents the image transformation scheme for the reasonable outcomes in CNN, obtained from practical RSSI with artificial Gaussian noise injection. Additionally, it presents an appropriate learning model with consideration of the characteristics of time series data. For the evaluation, a testbed is constructed, the practical raw RSSI is applied after the learning process, and the performance is evaluated with results of about 46.2% enhancement compared to the method employing only CNN.

中文翻译：

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基于 RSSI 分析的具有时间序列特征的卷积模型和马尔可夫转移场增强位置识别

尽管许多方案，包括基于学习的方法，都试图确定使用 RSSI 在室内环境中进行位置识别的解决方案，但它们都受到 RSSI 严重不稳定性的影响。与递归逼近神经网络获得的解决方案相比，使用基于考虑室内条件的特征提取的卷积神经网络 (CNN) 方法获得了各种最先进的解决方案。顺应这样的趋势，本研究提出了 CNN 中合理结果的图像转换方案，该方案是从具有人工高斯噪声注入的实际 RSSI 中获得的。此外，它还考虑了时间序列数据的特点，提出了一个合适的学习模型。为了评估，构建了一个测试平台，