High accuracy localization of objects is a crucial function for many modern applications, such as virtual and augmented reality, robotics, and self-driving cars, among others. This requires determining precise location of objects indoors, which is a challenging task. In recent years, Ultra-Wideband technology has seen increasing interest as a potential solution to this problem by the research community. This is mainly due to its innate capabilities of high update frequency and low power consumption which makes it a suitable technology for precise distance measurement and location determination. This study has aimed to answer what the state-of-the-art in the field of trilateration in Ultra-Wideband based indoor positioning systems utilizing other complementary technologies is. This was done by conducting a document survey using a Grounded theory approach for the analysis. To ensure validity and reliability of the study, the sample was collected through searching IEEE Xplore using different sets of keywords, and the potential samples was then checked using a data quality form. The analysis consisted of identifying categories and concepts in the sample. The analysis found that the Ultra-wideband based systems can achieve high positioning accuracy, but limitations such as non-line-of-sight disturbance must still be overcome for the technology to consistently achieve centimetre accuracy. These limitations are being mitigated using filtering, machine learning, and multi-sensory fusion. With these complementary technologies researchers can eliminate some of the limitations. The field does however seem to be in an exploratory stage where best practices for overcoming the current limitations are yet established.