# Recurrent Neural Network-Based Hybrid Localization for Worker Tracking in an Offshore Environment
## 4 Results
### Tracking Accuracy Test  
The tracking accuracy achieved by the RHLS was compared with that of a model built in a supervised manner using the ground truth location labels. For the positioning accuracy test of the four different decks, it was necessary to conduct the training and test for each deck separately. The average error distances were measured according to the time sequences of the test data. For fast training, eight data readings were processed simultaneously, and six readings for each route, that is, a total of six sets of data, were used for training. Two readings for each test were selected for each route, and training was terminated when the error of these data became less than 1.5. For comparison, the HMM-based Viterbi algorithm was evaluated by setting the maximum allowable single-step moving distance to 1.1 m. In addition, a commonly used k-nearest neighbor (kNN) method was used to compare the differences arising from continuous and static positioning (k was set to three). For the internal parameters of the Viterbi algorithm, the standard deviation of the stride was set to 0.6 m, the standard deviation of the heading was set to 45\({}^{\circ}\), the standard deviation of  
Figure 10: Collected radio maps for each deck: (**a**) main deck; (**b**) A deck; (**c**) first deck; (**d**) second deck. Figure 9: Hybrid positioning test space and access point (AP) beacon locations: (**a**) main deck; (**b**) A deck; (**c**) first deck; (**d**) second deck. Although there were some errors depending on the environment, we confirmed that RHLS exhibits sufficient location accuracy for the monitoring service despite the presence of diffuse reflections of the signal. This is because the result of localization through fingerprinting and tracking is corrected through the proximity and map matching techniques. Depending on the space, it was confirmed that the second deck, the hole-shaped space, exhibited the best positioning accuracy, and the corridor-type spaces exhibited relatively good positioning accuracy. The average error of the proposed method was 2.72 m, which is a good result compared to other methods. In addition, it can be seen that the time required for positioning was significantly reduced compared to other methods. Figure 13 compares the tracking accuracy of the sequence for each deck between the RHLS and the Viterbi method. It can be seen that the proposed method shows overall high accuracy regardless of the shape of the deck. In the case of a ship environment, signal transmission such as Wi-Fi is difficult because of the steel plate structure with a narrow corridor, and the signal fluctuation range is greater than that in a general land building owing to diffuse reflection. In addition, in the case of a ship, the AP environment is not sufficiently rich to derive high positioning accuracy, unlike a land building, to which various positioning methods are applied (such as References [28; 29]). Therefore, positioning accuracy is lower than that of a land building. The data collected and tested in this paper included all these environmental factors, and they were used in the experiment. Because the initial accuracy was not high, the most important factor was how much it was corrected through tracking. Although it can be seen that the tracking accuracy was corrected with time in the Viterbi method, it can be confirmed that the correction width was less than that of a general land building owing to unstable signal data. On the other hand, in the RHLS, the accuracy convergence was greater, and the data and features used in the LSTM learning process successfully reflected the special ship environment. This is especially noticeable on the second deck, which took the form of an open space. In the open space, it was difficult to use the specificity of the indoor structure; hence, the accuracy correction by PDR was relatively small.