In this technical report, we propose a framework for utilizing fixed, ultra-wideband ranging radio nodes to track a moving target node through walls in a cluttered environment. We examine both the case where the locations of the fixed nodes are known as well as the case where they are unknown. For the case when the fixed node locations are known, we derive a Bayesian room-level tracking method that takes advantage of the structural characteristics of the environment to ensure robustness to noise. We also develop a method using mixtures of Gaussians to model the noise characteristics of the radios. For the case of unknown fixed node locations, we present a twostep approach that first reconstructs the target node’s path and then uses that path to determine the locations of the fixed nodes. We reconstruct the path by projecting down from a higher-dimensional measurement space to the 2D environment space using non-linear dimensionality reduction with Gaussian Process Latent Variable Models (GPLVMs). We then utilize the reconstructed path to map the locations of the fixed nodes using a Bayesian occupancy grid. We present experimental results verifying our algorithm in an office environment, and we compare our results to those generated by methods using Extended Kalman Filter (EKF) SLAM and odometry mapping. Our algorithm is successful at tracking a moving target node and mapping the locations of fixed node
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