A research study aimed at developing a novel indoor positioning system is presented. The realized system prototype uses sensor fusion techniques to combine information from two sources: an in-house developed local Ultra-Wideband (UWB) radio-based ranging system and an inertial navigation system (INS). The UWB system measures the distance between two transceivers by recording the round-trip-time (RTT) of UWB radio pulses. Its principle of operation is briefly described, together with the main design features. Furthermore, the main characteristics of the INS and of the Extended Kalman Filter information fusion approach are presented. Finally, selected static and dynamic test scenario experimental results are provided. In particular, the advantages of the proposed information fusion approach are further investigated by means of a high dynamic test scenario.

This paper presents integration of ultrasonic and inertial approaches in indoor navigation system. Ultrasonic navigation systems allow to obtain good results whilst there are at least three beacon transmitters in the range of mobile receiver, but in many situations placement of large number of transmitters is not economically justified. In such situations navigation must be aided by other technique. This paper describes research on supporting ultrasonic system by inertial system based on Magnetic, Angular Rate and Gravity sensor. This can measure current orientation of the receiver and allows to estimate the length of the path by pedometer functionality.

The paper presents methods of on-line and off-line estimation of UAV position on the basis of measurements from its integrated navigation system. The navigation system installed on board UAV contains an INS and a GNSS receiver. The UAV position, as well as its velocity and orientation are estimated with the use of smoothing algorithms. For off-line estimation, a fixed-interval smoothing algorithm has been applied. On-line estimation has been accomplished with the use of a fixed-lag smoothing algorithm. The paper includes chosen results of simulations demonstrating improvements of accuracy of UAV position estimation with the use of smoothing algorithms in comparison with the use of a Kalman filter.