# Seamless Indoor-Outdoor Localization Through Transition Detection
## 1 Introduction  
Accurate and continuous location tracking has become a fundamental requirement for a wide range of applications, including navigation, asset management, emergency response, and smart city services. While outdoor positioning is largely achieved by satellite-based systems such as GPSs, these signals are often unreliable or unavailable indoors due to signal attenuation and multi-path effects. Conversely, indoor positioning systems relying on technologies such as WiFi, Bluetooth, or inertial sensors typically lack coverage or accuracy once users move outside. This fragmentation creates significant challenges for providing seamless, uninterrupted tracking as users transition between indoor and outdoor environments [1].  
Recent advances in sensor fusion, machine learning, and environmental awareness have enabled the development of integrated solutions that aim to bridge this gap. Systems such as IODetector in [2] and SUNS in [3] demonstrate the potential of combining data from multiple sources including GNSS, Wi-Fi, Bluetooth, and inertial sensors to achieve robust, user-friendly, and seamless tracking across diverse environments. However, despite these advances, ensuring high accuracy, low latency, and energy efficiency during indoor-outdoor transitions remains a complex problem, particularly in dynamic or infrastructure-limited settings.  
This paper proposes a seamless tracking algorithm specifically designed for Google Android smartphones for the continuous and uninterrupted tracking of a person or object as they move between indoor and outdoor environments, maintaining smooth and continuous trajectory estimation [4; 5; 6]. The proposed positioning system integrates measurement data from three primary sources: WiFi RSSI and BLE RSSI, inertial sensors (IMUs), and a GNSS. The user's environmental state, categorized as indoor, transition, or outdoor, is inferred using a combination of RSSI values, GNSS satellite information, and previously estimated positions. For indoor localization, the system employs a hybrid of WiFi-based fingerprinting and AI-augmented IMU tracking [7], with particle filtering applied in the post-processing stage. In contrast, the outdoor localization engine combines GNSS and AI-IMU data through an extended Kalman Filter (EKF) for robust estimation. During transitional phases, where both WiFi and GNSS signals tend to be unreliable, the system switches to AI-IMU tracking with a particle filter, operating without external signal sources.  
Accurately detecting the transition zone is crucial for seamless tracking, as the handover strategy, which is responsible for determining whether the device is in an indoor, outdoor, or transition state, significantly impacts overall tracking performance. The classification into these three states is based on RSSI signals, the previously estimated position, and GNSS signals. Notably, the RSSI signal model is derived using a neural network activation function. While machine learning models typically require training data, which are often gathered through supervised methods such as fingerprinting [8], this paper adopts an unsupervised data collection approach. In our method, the device collecting RSSI signals operates freely, without constraints such as being held in a fixed position. An overview of the entire positioning process is illustrated in Figure 1.  
This paper is structured as follows: Section 2 introduces some related works. Section 3 presents the handover strategy. Section 4 describes the separated positioning engines for indoor, transition, and outdoor. The experimental results and real-time verification results of the proposed model are analyzed in Section 5. The research directions and conclusions are proposed in Section 6.  
Figure 1: Overview of the proposed seamless indoor–outdoor localization.