Received Signal Strength Indicator (RSSI)-based indoor Location and Tracking (L&T) is a promising and challenging technology that enables mobile users/nodes to obtain their location information. This dissertation focuses on overcoming the challenges as well as improving the positioning accuracy for the RSSI-based L&T. In particular, the author considers 4 L&T solutions. In the first, the author develops a L&T solution by designing the Kalman Filter (KF) to work linearly within the positioning framework. To elaborate on this implementation, the equations of the KF are presented in a consistent manner with the implementation. In the second, the author designs a L&T solution based on the Iterated Extended Kalman Filter (IEKF) to improve the accuracy compared with the popular Extended Kalman Filter (EKF). In the third, the author overcomes the particular implementation challenges of the EKF by designing a L&T solution based on the implementation of the Scaled Unscented Transformation (SUT) to the KF. The author calls the resulting filter Scaled Unscented Kalman Filter (SUKF). In the forth, the author overcomes the implementation difficulties of the EKF by designing a L&T solution based on the implementation of the Spherical Simplex Unscented Transformation (SSUT) to the KF. The author calls the resulting filter the Spherical Simplex Unscented Kalman Filter (SSUKF). The proposed solutions with their corresponding achievements enhance the role of RSSI-based L&T in wireless positioning systems. The contributions led to significant improvement in the positioning accuracy, reliability and the ease of implementation.