Signal Space Based Navigation

Abstract

Researchers at the University of California, Davis have developed a navigation system that constructs a sensing map from wireless signal observations and pedestrian deadreckoning (PDR) data to enable accurate indoor navigation without relying on traditional geographic localization maps.

Full Description

This technology introduces a signal space based navigation apparatus and method that leverages wireless signal clusters, such as WiFi signals, and PDR displacement vectors obtained via crowd-sourced data from mobile user devices. Unlike conventional systems that depend on pre-existing localization maps linking geographic locations, this navigation system builds a topological sensing map that represents locations as clusters of wireless signals connected by mobility information. It detects turns using low-power accelerometers to collect more frequent wireless observations at these locations, improving both accuracy and energy efficiency. The system further uses density-based clustering (e.g., DBSCAN) and fuses PDR traces to establish relative positions between clusters, facilitating route planning and navigation guidance through a user interface.

Applications

• Indoor navigation solutions for shopping malls, airports, hospitals, and large public venues. 
• Enhanced location-aware services in environments with limited GPS accessibility. 
• Mobile applications providing crowd-sourced navigation data for retail and facility management. 
• Assistive technologies for visually impaired users in indoor public spaces. 
• Enterprise asset tracking and workforce navigation in industrial and commercial buildings.

Features/Benefits

• Removes the need for prior geographic knowledge or localization maps. 
• Compatible with existing mobile devices. 
• Constructs autonomous, non-intrusive maps using crowd-sourced wireless and PDR data. 
• Increases energy efficiency by activating higher sensor sampling rates only during detected turns with always-on accelerometers. 
• Mitigates PDR drift and signal fluctuation errors through clustering and trace fusion. 
• Refines wireless clusters adaptively over time to enhance sensing map accuracy. 
• Enables indoor navigation with segmented path instructions generated at turns. 
• Resolves signal fluctuation and PDR drift challenges in mobile device navigation. 
• Reduces energy consumption via adaptive, turn-based sensor sampling. 
• Overcomes complexity in mapping sensor data to physical layouts. 
• Simplifies indoor map building by removing the need for manual annotation or user intervention.

Patent Status