Localization Algorithms Using Tracking Approaches and Barometric Pressure Sensors in Indoor Environments

With the rapid development of positioning, localization, navigation, and self-driving car systems, the implementation of intelligent and robust localization systems for real-time location-based services (LBSs) has become increasingly attractive. This article presents high-performance positioning and tracking approaches characterized by a pipelined structure, high computational efficiency, flexibility, and real-time processing, implemented using field programmable gate arrays (FPGAs). In triangulation-based positioning approaches, estimated distance information is derived from communication signals and the path loss model, while vertical localization is achieved through the characteristics of barometric pressure (BP). After integrating positioning approaches with tracking methods and BP sensors, the results illustrate that the proposed localization algorithms closely estimate the trajectory of mobile devices. For FPGA-implemented algorithms, the proposed approaches effectively handle floating-point operations, reduce computing resource usage, and provide real-time processing capabilities, surpassing software-based designs and implementations. In terms of performance, the results demonstrate that the localization accuracy of the proposed hardware-based implementation is nearly identical to that of the software-based approach. Regarding vertical location accuracy, based on the proposed calibration approach, the BP value increases by 11.6 Pa for every one-meter decrease in altitude. To maintain floor-level accuracy over time despite atmospheric fluctuations, a real-time dynamic calibration mechanism using a fixed reference sensor is employed. In summary, the proposed localization algorithms, implemented with FPGAs and BP sensors, offer advantages such as lower circuit costs, higher processing efficiency, and reliable vertical location accuracy for real-time public safety LBS.