An enhanced indoor positioning system for first responders

Abstract

Localization and tracking support is useful in many contexts and becomes crucial in emergency response scenarios: being aware of team location is one of the most important knowledge for incident commander. In this work both localization and tracking for rescuers are addressed in the framework of REFIRE project. The designed positioning system is based on the wellknown prediction-correction schema adopted in field robotics. Proprioceptive sensors, i.e., inertial sensors and magnetometer, mounted on the waist of the rescuers, are used to form a coarse estimation of the locations. Due to the drift of inertial sensors, the position estimate needs to be updated by exteroceptive sensors, i.e., RFID system composed by tags embedded in the emergency signs as exteroceptive sensors and a wearable tag-reader. In long-lasting mission RFID tags reset the drift by providing a positioning having room-level accuracy.

Indoor positioning system using walking pattern classification

“…as proposed in [7], [2]. In the prediction step, the control input r k is obtained by gyroscopes measurements and the state transition is computed as follows:…”

“…It is worth underlying that data from accelerometers can be used only when the user is still, otherwise the gravity cannot be compensated. Moreover a validation gate based on Mahalanobis distance [2] is set up to discard magnetometers outliers due to soft or hard iron distortions.…”

“…The adopted IMU is based on MPU9150 chipset, available in most of the consumer smartphones. The IMU measurements have been pre-processed according to the procedure proposed in [2], in order to reduce the accelerometer and gyroscope bias and magnetometer interference.…”

Hybrid map building for personal indoor navigation systems

“…An Extended Kalman Filter is adopted to fuse data collected from gyroscope, accelerometer and magnetometer. This approach has been proposed in [19] and improved in [9], [10]: the attitude is calculated by estimating the corresponding quaternions during the motion. In the prediction step of the EKF, data collected from gyroscope are used as control input to elaborate the current attitude.…”

“…where a M and a m are the maximum and the minimum vertical acceleration during a step event and k is a parameter depending on the user and needs to be calibrated [9]. A map matching is used in the correction step.…”