[Paper Review] Indoor Localization Algorithm For Smartphones

Kaiqing Zhang, Hu Hong+3 more|arXiv (Cornell University)|Mar 26, 2015

Indoor and Outdoor Localization Technologies11 references3 citations

TL;DR

This paper proposes an area state-aided indoor localization algorithm for smartphones that fuses inertial measurements, WiFi RSSI, and indoor map data to improve positioning accuracy. By modeling user location through dynamic area states and using WiFi access points as proximity indicators, the system reduces drift and achieves a mean localization error of 1.6 meters in real-world experiments.

ABSTRACT

Increasing sources of sensor measurements and prior knowledge have become available for indoor localization on smartphones. How to effectively utilize these sources for enhancing localization accuracy is an important yet challenging problem. In this paper, we present an area state-aided localization algorithm that exploits various sources of information. Specifically, we introduce the concept of area state to indicate the area where the user is on an indoor map. The position of the user is then estimated using inertial measurement unit (IMU) measurements with the aid of area states. The area states are in turn updated based on the position estimates. To avoid accumulated errors of IMU measurements, our algorithm uses WiFi received signal strength indicator (RSSI) to indicate the vicinity of the user to the routers. The experiment results show that our system can achieve satisfactory localization accuracy in a typical indoor environment.

Motivation & Objective

• To address the challenge of drift and low accuracy in smartphone-based indoor localization using low-cost sensors.
• To integrate prior knowledge from indoor maps and WiFi access points to enhance localization robustness.
• To develop a real-time, practical indoor localization system suitable for standard smartphones.
• To reduce accumulated errors in dead-reckoning by leveraging spatial constraints and proximity indicators.

Proposed method

• Introduces the concept of 'area state' to represent a user's location within a specific region (e.g., corridor) on an indoor map.
• Uses dead-reckoning with accelerometer and gyroscope data to estimate step-by-step position and heading, with initial position and orientation assumed known.
• Applies map constraints to correct trajectory drift, especially at turning points and corridor junctions.
• Uses WiFi RSSI to detect proximity to access points, serving as absolute position indicators to correct drift.
• Employs a feedback loop where area states are updated based on position estimates, and position estimates are refined using map and WiFi data.
• Utilizes JOSM for map creation and calibration, with nodes, links, and reference points to represent spatial structure.

Experimental results

Research questions

• RQ1 Can area states derived from indoor maps effectively reduce drift in smartphone-based dead-reckoning?
• RQ2 How does combining WiFi RSSI proximity detection with map constraints improve localization accuracy?
• RQ3 What is the performance gain of using both map information and WiFi indicators compared to using either alone?
• RQ4 How robust is the system to sensor noise, step detection errors, and phone orientation variations?

Key findings

• The proposed algorithm reduces the average localization error from 4.2 meters (using only IMU) to 1.6 meters in real-world experiments.
• The system achieves a cumulative distribution function (CDF) where 90% of localization errors are below 2.5 meters when using both area states and WiFi indicators.
• Map constraints significantly reduce small-scale errors from sensor drift and step length miscalibration.
• WiFi RSSI proximity detection enables effective correction of position drift, especially in open areas or near access points.
• The system demonstrates robust performance in corridors due to map-based trajectory constraints, even with minor step misdetections.
• Bias in heading estimation (e.g., azimuth drift) is reduced to less than 2 degrees after calibration, improving long-term accuracy.