The invention belongs to the technical field of
Bluetooth indoor positioning, and relates to a
Bluetooth AOA and RSSI fusion indoor positioning method based on
deep learning, which comprises the following steps: step S10, a
Bluetooth positioning
base station receives a CTE
signal sent by a
signal source by using a BGAP protocol, and extracts I / Q data and RSSI data in the CTE
signal; step S20, the Bluetooth positioning
base station calculates the
azimuth angle, the
pitch angle and the distance of the signal by extracting the data, and uploads the
azimuth angle, the
pitch angle and the distance to the
proxy server through an
MQTT protocol; step S30, the PC subscribes to the
proxy server, downloads
azimuth angle,
pitch angle and distance data, performs Kalman filtering on the angle data, and performs
moving average filtering on the distance information; and S40, multiplying the filtered data by own
transpose, inputting the data into a
deep learning model for training, and finally outputting a
label position coordinate. The distance information of the RSSI and the azimuth angle and
pitch angle information of the AOA are fused, Kalman filtering and
moving average filtering are used for
processing data, and the specific position of the
label is predicted through a
deep learning network.