Advanced Bayesian Filtering Techniques for UWB Tracking Systems in Indoor Environments

Cooperative Localization in Wireless Networks

“…Both WiFi and RFID systems suffer from poor accuracy due to coarse measurements. On the other hand, UWB signals have a number of characteristics that make them more attractive for indoor localization, as well as for indoor communication in general [85]- [87]. The fine delay resolution of UWB signals is well suited for estimating propagation times (e.g., for RTOA or AOA), since the performance of delay estimation algorithms improves with increasing transmission bandwidth [23].…”

Particle filtering for UWB radar applications

“…its own equations (10)-(12) for the "update" and the "prediction". The result from every time step is the posterior density p(τ k |Z k ) as given in (9). Different criterions such as minimum mean square error (MMSE) or maximum a posteriori (MAP) can be used for the estimation of the delay vector τ k from the posterior density p(τ k |Z k ).…”

“…If the number of particles is sufficiently large, the estimates reach optimal Bayesian estimation. Particle filtering is commonly known to be used in localization application and tracking in dynamic scenarios [9]- [11]. Unlike a maximum likelihood estimator, the particle filtering algorithm is very flexible to model.…”

Enhanced UWB Indoor Tracking through NLOS TOA Biases Estimation

“…Contrarily to the classical random walk models used to describe the bias evolution in [10] and [9], we propose to include (6) in the state equation of an EKF.…”

“…Consequently, measured metrics and observed biases are spatially correlated. As a preliminary attempt in [10], EKF and particle filters have been considered with a random walk bias model that depends on the mobility and refreshment rate parameters. In [11], a particle filter enables to track bias variations modeled by a uniform distribution notched about the current bias values.…”