Efficient Localization via Soft Information With Generic Sensing Measurements

Accurate location awareness is essential for various context-based applications. This calls for efficient methodologies to collect, communicate and process position-dependent measurements, especially in situations with limited computational resources. The soft information (SI) approach has recently shown significant improvements in accuracy over conventional localization methods. By developing efficient SI-based techniques, it is possible to achieve higher precision also in case of stringent computational constraints. This paper proposes new SI-based localization techniques that utilize belief condensation and maximum entropy methods to reduce both communication burden and computational complexity. In addition, the techniques presented enable the use of generic sensing measurements, including those taking discrete and categorical values. Through two case studies involving time and angle measurements, we demonstrate how the proposed approach can significantly improve localization accuracy and computational efficiency.