Indoor Localization—building spatial context for the Alexa ecosystem

Indoor positioning using GPS is unreliable in enclosed environments, yet spatial context remains essential for enabling intelligent interactions with voice assistants and other connected devices. We introduced a foundational software framework for real-time spatial awareness in indoor environments, leveraging existing wireless infrastructure and devices' sensing capabilities. By utilizing commodity radios such as Bluetooth Low Energy (BLE), Wi-Fi, Zigbee, and Ultra-Wideband (UWB), as well as other modalities including ultrasound and inertial tracking, the system performs distance estimation and positioning. It abstracts complex RF-based algorithms into a unified interface, supporting spatial use cases including proximity detection, device-to-device ranging, spatial presence, and user tracking. The framework has been developed through extensive real-world experimentation, systematically evaluating the performance, reliability, and limitations of each wireless modality under dynamic environmental conditions. To ensure scalability and robustness, the system integrates techniques from signal processing, machine learning, and edge computing. It supports multimodal sensor fusion to accommodate increasing heterogeneity in device form factors and sensing capabilities.

While not directly exposed to end users, this platform acts as a core enabler for higher-layer applications that require spatial context, including room-aware assistance, presence sensing, and personalized user experiences. It integrates with LLM-based Alexa+, providing it with location context to facilitate more intelligent and adaptive behaviors. This work contributes to the broader vision of ambient computing by enabling distributed intelligence across smart devices, with applications spanning smart homes, automotive systems, and retail environments.