Ultra-wide bandwidth (UWB) and Inertial Navigation (IN) have been adopted in high precision localization and tracking systems. However, the ranging measurements influenced by non-line-of-sight (NLOS) path may degrade the localization accuracy. Besides, inertial measurement errors are within the sensing data and constantly accumulate over the time. To overcome the above problems, we investigate NLOS mitigation for UWB measurements, and calibration for IN estimations. Both ranging measurement and step length estimation are modeled using Gaussian mixture model (GMM), and one maximum likelihood (ML) estimator is developed. Then, the non-convexity estimator is relaxed into a semidefinite programming (SDP), which global minimum can be readily attained. Finally, both simulation and experimental results are provided to illustrate the validity and performance of our proposed SDP-based localization and tracking algorithm.
['Yueyue Zhang', 'Yaping Zhu', 'Feng Yan', 'Zhengquan Li', 'Lianfeng Shen']
Non-line-of-sight mitigation in wireless localization and tracking via semidefinite programming