Precision Localization Algorithms in Wireless Indoor Positioning

In the field of wireless indoor positioning, precision localization algorithms represent a crucial research direction. Among these, Chan's algorithm serves as a commonly used approach that primarily relies on signal strength for positioning. The algorithm typically involves calculating time difference of arrival (TDOA) measurements and solving hyperbolic positioning equations through least-squares estimation. However, due to signal strength being affected by multiple factors such as signal propagation paths and obstacles, its accuracy faces certain limitations. To enhance indoor positioning precision, current research focuses predominantly on utilizing multiple sensors for localization, including WiFi, Bluetooth, and inertial measurement units (IMU). This multi-sensor fusion positioning technology typically employs Kalman filters or particle filters to combine different data sources, achieving improved accuracy through sensor complementarity. Such techniques have been widely applied in smart homes, intelligent office systems, and smart healthcare domains. Implementation often involves coordinate transformation algorithms and sensor calibration procedures to ensure system robustness. We hope this technical information proves beneficial for your development projects.