PythaPosi: Indoor Location Estimation with Physics Constraint and Recursive Filtering
Here, we introduce the PythaPosi module that improves placement accuracy when performing indoor positioning using Bluetooth low energy (BLE) beacons. To achieve higher accuracy than conventional algorithms, the PythaPosi implementation integrates trilateration with two methods. The first method is to use classical physics constraints, such as client speed and acceleration. Generally, receiving signals from beacons is unstable, so some of the signals are errors, forcing us to reduce such error signals through physical constraints. The second method is recursive filtering to smooth with recursive factors, so the previous value is measured recursively in the current location. Furthermore, this method provides an excellent indoor positioning base because it facilitates easy simulations and can also be used to provide a base for interfacing with other positioning methods.
KeywordsIndoor location Trilateration BLE Bluetooth, beacon RSSI Classical physics constraints Recursive filter
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