System and method for calibration-lessly compensating bias of sensors for localization and tracking

A probabilistic motion model to calculate the motion parameters of a user's hand held device using a noisy low cost Inertial Measurement Unit (IMU) sensor. Also described is a novel technique to reduce the bias noise present in the aforesaid IMU sensor signal, which results in a better performance of the motion model. The system utilizes a Particle Filter (PF) loop, which fuses radio signal data accumulated from Bluetooth Low Energy (BLE) beacons using BLE receiver with the signal from IMU sensor to perform localization and tracking. The Particle Filter loop operates based on a Sequential Monte Carlo technique well known to persons of ordinary skill in the art. The described approach provides a solution for both the noise problem in the IMU sensor and a motion model utilized in the Particle Filter loop, which provides better performance despite the noisy IMU sensor.