# Combining Dilution of Precision and Kalman Filtering for UWB Positioning in a Narrow Space
## 4 Discussion  
In this study, an indoor positioning algorithm combining DOP and KF was proposed for the narrow space. Comparative experiments were carried out on different scenes and different algorithms. The algorithm was also tested in both static and dynamic terms. The following will discuss the characteristics of DOP in a narrow space and the reasons behind the positioning results. In the experiments of DOP numerical analysis model, the DOP values of different scenes were compared. We found that the DOP values in a narrow space were much higher than that in a wide space. This phenomenon was consistent with some existing research reports [48; 49]. In general, a PDOP value of 3 or less and an HDOP value of 1 or less indicate a good base station layout [50], where the positioning solution can meet the requirements of high-precision positioning [8]. However, in a narrow corridor, the angles between the tag and different base stations are quite close, resulting in small unit vector geometry [16]. Under this circumstance, the small ranging error will cause great positioning error. Therefore, the UWB positioning accuracy of a narrow space will become very low. In the positioning experiments, the positioning results of three algorithms in different narrow scenes were compared. The fusion algorithm had better positioning accuracy and stability than the other two algorithms. According to the DOP numerical analysis experimental results and existing research conclusions [16], in the narrow space, the positioning error mainly comes from the special spatial structure of the space (the DOP values are high). Since the DOP values reflect the influence of the ranging error on the positioning accuracy, the larger DOP values mean that it is easy to introduce more noise during positioning [51]. However, previous studies have only improved the positioning accuracy from the perspective of the algorithm, ignoring the impact of the scene itself [52; 53]. Through the DOP experiments on different scenes and the parameter analysis on KF, we found that the DOP values were positively correlated with the observation noise parameter value of KF. Therefore, this study contributed to the existing studies and tried to adaptively introduce the DOP values of the target scene into KF. According to the experimental results in both static and dynamic terms, the proposed method adaptively suppressed the error by introducing the DOP values into the KF algorithm. Furthermore, we try to explain some specific phenomena in the two positioning experiments. In the static positioning experiments, for the T4 point in Figure 8, the positioning accuracy of the three algorithms was basically the same, and the positioning error of T4 point was far lower than that of other points. This was because T4 was closer to the center point; thus, the weight of the WLS was not much different from that of the LS at this time, and thus, the positioning results of the two algorithms were basically the same. Furthermore, the fusion algorithm was improved on the basis of WLS, and the DOP value at T4 is relatively small. The phenomenon further proves that the proposed algorithm is more likely to improve the positioning accuracy for the points with higher DOP values. In the dynamic positioning experiment, in Scene A, when the tag position was solved by WLS, the positioning trajectory was not consistent with the real trajectory and produced large oscillation. This was because the value distribution of DOP in this scene was very large, and even a small ranging error would cause a serious positioning error. Meanwhile, in a confined space such as Scene A, there was a large amount of reflection and diffraction of electromagnetic wave propagation, thus causing a serious multipath effect [14; 15]. This degraded the ranging performance of UWB [54]. As a result, the trajectory obtained by WLS solution at this time differed greatly from the real trajectory. However, when the fusion algorithm was used to solve the tag position, the trajectory was much better than the WLS result.