Chemistry

Indoor positioning system

Fingerprint (computing)

Convolutional neural network

Engineering

Artificial intelligence

Biochemistry

Autoencoder

Structural engineering

Pattern recognition (psychology)

Positioning system

Gene

Operating system

Computer science

Real-time computing

Key (lock)

Accelerometer

Computer vision

Computer security

Artificial neural network

Node (physics)

Robustness (evolution)

Sensors

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.

CCpos: WiFi Fingerprint Indoor Positioning System Based on CDAE-CNN