Statistics

Dead reckoning

Machine learning

Data mining

Engineering

Philosophy

Artificial intelligence

Systems engineering

Transport engineering

Key (lock)

Pedestrian

Phone

Computer vision

Mathematics

Independence (probability theory)

Computer security

Computer science

Linguistics

Global Positioning System

Telecommunications

Estimation

IEEE Transactions on Instrumentation and Measurement

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.

Accurate Step Length Estimation for Pedestrian Dead Reckoning Localization Using Stacked Autoencoders