## 6 Conclusions  
The 5G system could provide accurate multiple TDOA measurements and single TOA measurement, which could improve UE positioning accuracy. However, existing closed-form localization algorithms are designed for measurements having the same variance, and not suitable for heteroscedastic TDOA and TOA measurements.  
In this paper, a three-step WLS closed-form localization algorithm is developed for the nonlinear equation set of those hybrid heteroscedastic measurements. The algorithm is an extension of Chan-Ho algorithm [17]. The first WLS provides an initial position for the last two steps. Then the algorithm uses two WLSs to estimate position based on heteroscedastic TDOA and TOA measurements. Moreover, after the GDOP of this hybrid positioning is derived, it was proven that this hybrid positioning has a higher theoretical higher bound of accuracy than TDOA-only positioning.  
The simulation scenario is built according to the real situation. By comparing with two TDOA-only closed-formed localization algorithm, Chan-Ho [17] and BiasRed [18], and CRLB [35] of TDOA-only positioning and the proposed hybrid positioning in the same simulation scenario, the results show that the proposed localization algorithm has better performance than the two closed-form TDOA-only positioning methods, and the positioning accuracy approaches CRLB when the TDOA measurement errors are small. In different areas of the scenario, the proposed positioning algorithm has a different improvement in positioning accuracy. In the area surrounded by the ANTs, the proposed algorithm shows only a slight improvement effect. However, in the rest of the area, the accuracy of the proposed algorithm is even higher than the CRLB of TDOA-only positioning. The simulations indicates that using the hybrid TDOA and TOA measurements as well as the localization algorithm proposed in this paper have the potential to effectively improve the positioning accuracy of the indoor distribution system.  
The proposed localization algorithm and the two compared algorithms, Chan-Ho and BiasRed, were constructed and simulated according to the ranging errors under the line-of-sight wireless propagation channel. In real scenes, non-line-of-sight channels are ubiquitous, which may cause the positioning accuracy of these algorithms to decrease. In future work, the analysis and improvement of the proposed algorithm in the non-line-of-sight environment can be studied. Meanwhile, the proposed algorithm is only confirmed by simulation for a specific environment. The actual algorithm performance should be investigated in future work.  
Conceptualization, H.W., L.Y. and S.T.; Data curation, H.W.; Formal analysis, H.W.; Funding acquisition, Z.D. and F.Y.; Investigation, H.W. and X.Z.; Methodology, H.W.; Project administration, Z.D. and F.Y.; Resources, H.W. and X.Z.; Software, H.W. and X.Z.; Supervision, Z.D.; Validation, H.W. and X.F.; Visualization, H.W.; Writing--Original draft, H.W.; Writing--Review & editing, H.W., X.F. and S.T.  
This work was financially supported by the National Key Research & Development Program under Grant No. 2018 YFC0809702 and No. 2016 YFB0502001.  
The authors would like to express their thanks to [PERSON] for help in the simulation and [PERSON] for the help in the paper's revisions.  
The authors declare no conflict of interest.