Research Article
An Indoor and Outdoor Positioning Using a Hybrid of Support Vector Machine and Deep Neural Network Algorithms
Table 2
Positioning accuracies of the SVM-DNN algorithm in different ranges.
| | Scenarios | Estimated errors in different ranges (m) | Average Errors (m) | | <0.50 m | <0.75 m | <0.90 m | <0.95 m |
| | Scenario 1 (%) | 57.14% | 78.58% | 95.71% | 100% | 0.48 | | Scenario 2 (%) | 54.29% | 75.71% | 97.14% | 100% | 0.49 | | Scenario 3 (%) | 51.43% | 77.14% | 92.86% | 98.57% | 0.51 | | Scenario 4 (%) | 50% | 70% | 90% | 95.71% | 0.53 |
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