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在室内定位系统中提高定位精度和减少处理时间:对AI模型进行比较分析
Salwa Sahnoun1,2, Rihab Souissi1,2,3, Sirine Chiboub1,2,3
1Laboratory of Signals, Systems, Artificial Intelligence and Networks (SM@RTS), Digital Research Center of Sfax (CRNS), Sfax University, Sfax 3021, Tunisia.
Sensors (Basel, Switzerland)
|January 25, 2025
概括
循环神经网络 (RNN) 模型为室内定位系统提供了卓越的性能,实现了高定位精度和快速处理时间. 这项研究比较了各种AI模型,发现RNN是移动跟踪中最有效的.
科学领域:
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
- 传感器融合式传感器
背景情况:
- 室内定位系统 (IPS) 对于移动跟踪和导航至关重要.
- 现有的IPS经常面临准确性和处理速度方面的挑战.
- 人工智能模型为增强IPS性能提供了潜力.
研究的目的:
- 为了比较评估室内定位的AI模型.
- 评估不同AI算法的本地化准确性和处理时间.
- 为了确定移动跟踪应用程序的最佳AI模型.
主要方法:
- 人工神经网络 (ANN),长期短期记忆 (LSTM),循环神经网络 (RNN) 和卡尔曼过器的比较分析.
- 使用了接收信号强度指标 (RSSI) 和9轴ICM-20948传感器数据.
- 实施数据清理和特征选择技术以减少错误.
主要成果:
- 循环神经网络 (RNN) 模型表现出最佳性能.
- 实现了0.247m的定位错误,处理延迟为0.077s.
- 在12米×9.5米的区域内使用四个杆评估性能.
结论:
- 循环神经网络 (RNN) 对室内定位非常有效.
- 基于测试和验证数据的模型选择对于有效的移动跟踪至关重要.
- 人工智能的进步显著提高了IPS的本地化准确性和效率.
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