The use of MEMS-based inertial sensors for a relative positioning estimation system (RPES) was investigated. A number of data acquisition and processing techniques are developed and tested, to determine which one would provide the best performance of the proposed method. Because inertial-based sensors don’t rely on other references to calibrate their position and orientation, there is a steady accumulation of errors over time. The errors are caused by various sources of noise such as temperature and vibration, and the errors are significant. This work investigates various methods to increase the signalto- noise ratio, in order to develop the best possible RPES method. The main areas of this work are as follows:​ (i) The proposed RPES application imposes specific boundary conditions to the signal processing, to increase the accuracy. (ii) We propose that using redundant inertial rate sensors would give a better performance over a single rate sensor. (iii) We investigate three Kalman filter algorithms to accommodate different combinations of sensors:​ Parallel sensors arrangement, Series sensors arrangement, and compression arrangement. In implementing these three areas, the results show that there is much better improvement in the data in comparison to using regular averaging techniques.