Research Article
An Indoor and Outdoor Positioning Using a Hybrid of Support Vector Machine and Deep Neural Network Algorithms
Table 3
Positioning accuracies of the SVM-DNN algorithm in different ranges.
| | Accuracy at different scenarios | Error boundaries (m) | Average errors (m) | RMSE | | <0.9 m | <1 m | <1.5 m |
| | Scenario 1 (%) | 62.5% | 62.5% | 100% | 0.89 | 0.34 | | Scenario 2 (%) | 50% | 62.5% | 87.5% | 0.95 | 0.37 | | Scenario 3 (%) | 37.5% | 50% | 87.5% | 0.99 | 0.38 | | Scenario 4 (%) | 50% | 50% | 75% | 1.05 | 0.42 |
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