Abstract
WiFi is widely used for indoor positioning because of its advantages such as long transmission distance and ease of use indoors. To improve the accuracy and robustness of indoor WiFi fingerprint localization technology, this paper proposes a positioning system CCPos (CADE-CNN Positioning), which is based on a convolutional denoising autoencoder (CDAE) and a convolutional neural network (CNN). In the offline stage, this system applies the K-means algorithm to extract the validation set from the all-training set. In the online stage, the RSSI is first denoised and key features are extracted by the CDAE. Then the location estimation is output by the CNN. In this paper, the Alcala Tutorial 2017 dataset and UJIIndoorLoc are adopted to verify the performance of the CCpos system. The experimental results show that our system has excellent noise immunity and generalization performance. The mean positioning errors on the Alcala Tutorial 2017 dataset and the UJIIndoorLoc are 1.05 m and 12.4 m, respectively.
Highlights
We evaluate the performance of the CCpos system on two publicly available datasets: UJIIndoorLoc [29] and the Alcala Tutorial 2017 dataset [30]
95.3%, the building location accuracy is 99.6%, and the mean positioning error is 12.4 m) when the model parameters are shown in Table 8, the K-mean algorithm has the clustering center K = 2000, and the received signal strength indicator (RSSI) uses the zero-to-one normalization method
We propose the CCpos positioning system, a WiFi fingerprint positioning system based on convolutional denoising autoencoder (CDAE) and convolutional neural network (CNN)
Summary
Receiving signal strength [10] to obtain the necessary distance and angle information This kind of method is a positioning technology based on infrastructure, which requires. This paper proposes a positioning system using CDAE and CNN, and utilizes K-means clustering algorithm to partition the data set in the offline stage, so that this positioning system has high localization accuracy even on small datasets. It solves the problem that the random selection method may lead to fluctuations in localization due to incomplete data coverage of the training set in the case of small data sets This system designs a new network model that combines CDAE and CNN.
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