Ultrawideband (UWB) based localization has the potential to be used in a variety of applications due to its high accuracy. For robust and high performance in real environment, the most challenging issue is the detection and mitigation of noise from non-line-of-sight (NLOS) signals. Current researches use channel state information, particle or Kalman filter, and statistics based approaches for the NLOS noise detection and mitigation. These solutions show high accuracy in some applications; however, they need additional hardware and work in static environment only. We propose an NLOS mitigation algorithm that does not need any additional hardware andworks in dynamic environment with mobile obstacles. The main idea of the algorithm are to estimate the NLOS bias by repetitively comparing the intersection of the hyperbolas with intersections of circles. The proposed approach is tested for Decawave UWB testbed. Experimental results show that the proposed scheme works well in different dynamic scenarios as compared to the localization scheme without NLOS noise mitigation.

WLAN based localization is a key technique of location‐based services (LBS) indoors. However, the indoor environment is complex; received signal strength (RSS) is highly uncertain, multimodal, and nonlinear. The traditional location estimation methods fail to provide fair estimation accuracy under the said environment. We proposed a novel indoor positioning system that considers the nonlinear discriminative feature extraction of RSS using kernel local Fisher discriminant analysis (KLFDA). KLFDA extracts location features in a well‐preserved kernelized space. In the new kernel featured space, nonlinear RSS features are characterized effectively. Along with handling of nonlinearity, KLFDA also copes well with the multimodality in the RSS data. By performing KLFDA, the discriminating information contained in RSS is reorganized and maximally extracted. Prior to feature extraction, we performed outlier detection on RSS data to remove any anomalies present in the data. Experimental results show that the proposed approach obtains higher positioning accuracy by extracting maximal discriminate location features and discarding outlying information present in the RSS data.