Name
Title | ||
|---|---|---|
Accurate Step Length Estimation for Pedestrian Dead Reckoning Localization Using Stacked Autoencoders |
Abstract | ||
|---|---|---|
Pedestrian dead reckoning (PDR) is a popular indoor localization method due to its independence of additional infrastructures and the wide availability of smart devices. Step length estimation is a key component of PDR, which has an important influence on the performance of PDR. Existing step length estimation models suffer from various limitations such as requiring knowledge of user’s height, lack of consideration of varying phone carrying ways, and dependence on spatial constraints. To solve these problems, we propose a deep learning-based step length estimation model, which can adapt to different phone carrying ways and does not require individual stature information and spatial constraints. Experimental results show that the proposed method outperforms existing popular step length estimation methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/tim.2018.2871808 | IEEE Transactions on Instrumentation and Measurement |
Keywords | Field | DocType |
Estimation,Accelerometers,Legged locomotion,Gyroscopes,Machine learning,Acceleration,Adaptation models | Pedestrian,Gyroscope,Accelerometer,Real-time computing,Electronic engineering,Dead reckoning,Phone,Acceleration,Artificial intelligence,Deep learning,Mathematics | Journal |
Volume | Issue | ISSN |
68 | 8 | 0018-9456 |
Citations | PageRank | References |
6 | 0.43 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fuqiang Gu | 1 | 38 | 3.56 |
| Kourosh Khoshelham | 2 | 65 | 12.67 |
| Chunyang Yu | 3 | 30 | 3.70 |
| Jianga Shang | 4 | 33 | 5.04 |