Research Article
Hybrid Indoor-Based WLAN-WSN Localization Scheme for Improving Accuracy Based on Artificial Neural Network
Table 2
Comparison between the proposed hybrid indoor positioning system and previous hybrid indoor positioning studies.
| Reference | Hybrid position systems | Type of study | Test area | Total number of anchor points | Accuracy | Implementation |
| [12] | WSN + RFID | Experimental | 5 30 m | 148 (approx.) | 1.6 m | Fingerprinting + extended Kalman filter approach | [29] | WiFi + bluetooth | Experimental & simulation | 50 m | 24 | 1.75 m | Fingerprinting approach | [30] | WiFi + RFID | Experimental | 25 12 m | 80 (approx.) | 1.6 m | Fingerprinting + particle filter approach | Our WiFi data (alone) | WiFi | Experimental | 25 28 | 228 | 1.22 | Fingerprinting + artificial neural network | Our WSN data (alone) | WSN | Experimental | 10 28 | 96 | 1.36 | Fingerprinting + artificial neural network | Proposed hybrid indoor system | WiFi + WSN | Experimental | 10 28 m | 96 | 1.05 m | Fingerprinting + artificial neural network |
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