Abstract
By collecting the magnetic field information of each spatial point, we can build a magnetic field fingerprint map. When the user is positioning, the magnetic field measured by the sensor is matched with the magnetic field fingerprint map to identify the user’s location. However, since the magnetic field is easily affected by external magnetic fields and magnetic storms, which can lead to “local temporal-spatial variation”, it is difficult to construct a stable and accurate magnetic field fingerprint map for indoor positioning. This research proposes a new magnetic indoor positioning method, which combines a magnetic sensor array composed of three magnetic sensors and a recurrent probabilistic neural network (RPNN) to realize a high-precision indoor positioning system. The magnetic sensor array can detect subtle magnetic anomalies and spatial variations to improve the stability and accuracy of magnetic field fingerprint maps, and the RPNN model is built for recognizing magnetic field fingerprint. We implement an embedded magnetic sensor array positioning system, which is evaluated in an experimental environment. Our method can reduce the noise caused by the spatial-temporal variation of the magnetic field, thus greatly improving the indoor positioning accuracy, reaching an average positioning accuracy of 0.78 m.
Highlights
Location-based service (LBS) is a value-added service that uses positioning technology to precisely provide user location information
When the user is positioning, the magnetic field measured by the sensor is matched with the magnetic field fingerprint map to identify the user’s location
Since the magnetic field is affected by external magnetic fields and magnetic storms, which can lead to “local temporal-spatial variation”, this makes it difficult to construct a stable and accurate magnetic field fingerprint map
Summary
Location-based service (LBS) is a value-added service that uses positioning technology to precisely provide user location information. As for visual orientation, this technology uses cameras to measure the distance between objects, calculating their latent information; its advantage is having high accuracy, but the disadvantage is that the positioning range is limited by the camera’s viewing angle and distance, it is not suitable for large-scale positioning tasks It is affected by light, causing it to perform unstably in conditions in which light changes drastically. We use three MMC5883MA magnetic sensors to build a magnetic sensor array and develop a low-complexity recurrent probabilistic neural network classifier [16] on the embedded platform “STM32F767ZI” to calculate the position information, and complete a high-accuracy, low-power consumption indoor positioning system In this system, we use the Kalman filter to filter out the environmental noise of the magnetic field signal output by our magnetic sensor array and calculate the differentiation computation value between the sensors to reduce the influence of the time changing of the indoor magnetic field. Because the magnetic north is not the same as the geographic north pole, this deviation angle is regarded as the magnetic declination
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