Indoor positioning technology is more and more widely used in daily life, such as indoor navigation, smart home, shopping mall advertisement, etc. This article will detail the principles of this technique and its advantages in practical applications.

principle

The basic principle is to obtain the distance between the device and the beacon in the indoor environment by deploying several Bluetooth beacons, and then combine the current location information of the device collected by the built-in sensors (such as accelerometers, gyroscopes, etc.) of the mobile phone, and input these data into It is processed in the Kalman filter algorithm, and finally the optimized device location information is output to improve positioning accuracy and real-time performance.

Deploy Bluetooth Beacons

Deploy several Bluetooth beacons indoors, and record the location coordinates and signal strength values of each beacon. Because the signal penetration ability of Bluetooth is strong and can pass through obstacles, Bluetooth positioning is widely used in indoor environments.

Data collection by mobile phone hardware

Use hardware devices such as built-in sensors (such as accelerometers, gyroscopes, etc.) in the mobile phone to collect the current location information of the device, such as direction, moving speed, angle, etc.

Bluetooth positioning to obtain data

Use the Bluetooth module of the mobile phone to scan the surrounding Bluetooth beacons and obtain their signal strength values. Based on the signal strength value, the distance between the device and the beacon is calculated, and then the location of the device is determined.

Kalman filter algorithm to process data

The collected location information and signal strength values are input into the Kalman filter algorithm for processing, and the location information of the device is optimized by means of weighted average and sliding window to improve the positioning accuracy and real-time performance. The Kalman filter algorithm is a commonly used data prediction and processing algorithm, which can effectively deal with the noise and errors existing in sensor data.

Data Fusion

The position information output by the Kalman filter algorithm is fused with other positioning technologies (such as WiFi positioning) to further improve the positioning accuracy and accuracy. The fusion of multiple positioning technologies can eliminate the disadvantages of a single technology while making full use of their respective advantages.

The indoor positioning technology based on Bluetooth positioning, mobile phone sensors and Kalman filter has the characteristics of high positioning accuracy, good real-time performance and low cost. This technology has important application value in smart home, shopping mall advertising, indoor navigation and other fields. However, in the application process, there are also many challenges, such as the noise interference of sensor data and the uncertainty of position information, etc., which need to be further studied and solved.