A hybrid smartphone indoor positioning solution for mobile LBS

Area of Science:

• Mobile computing
• Indoor positioning systems
• Sensor fusion

Background:

• Smartphone positioning is crucial for navigation and location-based services (LBS).
• Existing solutions often rely on deterministic motion models, limiting applicability.
• Integrating indoor positioning with mobile LBS presents technical challenges.

Purpose of the Study:

• To present HIPE, a novel smartphone indoor positioning engine for mobile LBS.
• To develop a hybrid solution fusing smartphone sensor measurements and wireless signals.
• To enable flexible and cost-efficient indoor positioning across diverse smartphone platforms.

Main Methods:

• Utilizing smartphone sensors to measure motion dynamics information (MDI).
• Employing hidden Markov model (HMM) algorithms (grid-based filter, Viterbi) for data fusion.
• Developing a hybrid approach combining MDI and wireless signal data.

Main Results:

• HIPE demonstrates adequate positioning accuracy and robustness in various scenarios.
• The system is widely applicable across different motion scenarios, unlike previous methods.
• Positioning reliability increases with higher precision of motion dynamics information.

Conclusions:

• HIPE offers a cost-efficient and flexible indoor positioning solution for mobile LBS.
• The hybrid approach overcomes limitations of deterministic motion models.
• The system's adaptability to different smartphone sensors enhances its practical utility.