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Target positioning and tracking become a very challenging topic for indoor applications. To improve the accuracy of indoor position tracking, it is useful to acquire map descriptions of indoor environments, with two major paradigms: accurate metric map (Mmap) and topological map (Tmap). Research has shown the efficiency of using Tmap for positioning, but often poses difficulty to explicitly represent...
Bayesian estimation methods are widely used for wireless localization systems. They employ priori information and current measurement error distribution models to derive the state of a mobile target. Cramér-Rao lower bound (CRLB) is a fundamental tool to analyze the performance of Bayesian estimators. Although CRLB is derived based on the measurement error distribution, only a few works have investigated...
A large number of indoor positioning systems are based on sensor networks or WLAN ranging techniques with a filter to remove the positioning uncertainty coming from the ranging errors. Bayesian filter has emerged as a useful approach for sequential position estimation, which generally resorts to a numerical solution due to the nonlinearity and the non-Gaussian nature of mobile positioning. The accuracy...
Numerical methods for recursive Bayesian estimation are widespread in position tracking of robotics. However, challenges arise to indoor network positioning due to the limited processing power and inaccurate ranging measurements of low-end network nodes. For efficient and robust indoor position tracking, we incorporate a recursive bound to a grid-based filter namely RBGF, which approximates the posterior...
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