This dissertation introduces Inverse Synthetic Array Reconciliation Tomography (ISART), an algorithm that exploits the short-time accuracy of inertial navigation systems (INS) and the time-stability of radio frequency (RF) positioning algorithms to achieve a high level of positioning accuracy. Novel array processing and data fusion techniques are employed to acheive performance far greater than RF and INS algorithms previously developed. This research is directed toward addressing the need for a viable tracking solution for firefighters and other first responders in urban and indoor environments. The approaches in this work are fundamentally different from other RF-INS fusion approaches, in the way we combine INS data with RF data. Rather than simply fusing the measurements from two systems that are estimating position (or states directly related to position) we use the inertial navigation data to improve the accuracy of our RF estimates at the signal level, before integrating them into an overall fusion system through the use of an extended Kalman filter (EKF). This work outlines the theoretical basis for ISART, and shows the results of simulations that support the claimed accuracy improvement of the ISART algorithm over existing methods. The viability of ISART in real world settings is then examined through the results of three field tests what were conducted in support of this research.