Estimating position and orientation change of a mobile platform from two consecutive point clouds provided by a high-resolution sensor is a key problem in autonomous navigation. In particular, scan matching algorithms aim to find the translation and rotation of the platform such that the two point clouds coincide. The association of measurements in point cloud one with measurements in point cloud two is a problem inherent to scan matching. Existing methods perform non-probabilistic data association, i.e., they assume a single association hypothesis. This leads to overconfident pose estimates and reduced estimation accuracy in ambiguous environments. Our probabilistic scan matching approach addresses this issue by considering all association hypotheses with their respective likelihoods. We formulate a holistic Bayesian estimation problem for both data association and pose inference and present the corresponding joint factor graph. Near-optimum maximum a posteriori (MAP) estimates of the sensor pose are computed by performing iterative message passing on the factor graph. Our numerical study shows performance improvements compared to non-probabilistic scan matching methods that are based on the normal distributions transform (NDT) and implicit moving least squares (IMLS).

This paper addresses the problem of multitarget tracking using a network of mobile sensors with unknown positions. In contrast to commonly-used approaches which split the sensor localization and target tracking into two different sub-problems, we propose a holistic approach for joint localization and tracking. The theory of graphical models is used to describe the statistical relationship between sensors, targets, and measurements. To jointly infer the states of sensors and targets, we use the statistical processing of belief propagation.

In the past, NLOS propagation was proven to be a source of distortion for radio-based positioning systems due to the lack of temporal and spatial resolution of previous cellular systems. Hence, every NLOS component was perceived as a perturbation for localization. Even though 5G is not yet standardized, a strong proposal, which has the potential to overcome the problem of limited temporal and spatial resolution, is the massive MIMO millimeter wave technology. We reconsider the role of NLOS components for position and orientation estimation in 5G millimeter wave MIMO systems. Our analysis is based on the concept of Fisher information. We show that for sufficiently high temporal and spatial resolution, NLOS components always provide position and orientation information that consequently increase position and orientation estimation accuracy. In addition, we show that the information gain of NLOS components depends on the actual location of the reflector or scatter. Our numerical examples suggest that the NLOS components are most informative about the position and orientation of a mobile terminal when the corresponding reflectors or scatterers are illuminated with narrow beams.

Millimeter wave signals with multiple transmit and receive antennas are considered as enabling technology for enhanced mobile broadband services in 5G systems. While this combination is mainly associated with achieving high data rates, it also offers huge potential for radio-based positioning. Recent studies showed that millimeter wave signals with multiple transmit and receive antennas are capable of jointly estimating the position and orientation of a mobile terminal while mapping the radio environment simultaneously. To this end, we present a message passing-based estimator which jointly estimates the position and orientation of the mobile terminal, as well as the location of reflectors or scatterers in the absence of the line-of-sight path. We provide numerical examples showing that our estimator can provide considerably higher estimation accuracy compared to a state-of-the-art estimator. Our examples demonstrate that our message passing-based estimator neither requires the presence of a line-of-sight path nor prior knowledge regarding any of the parameters to be estimated.

We consider cooperative position estimation in wireless networks as Bayesian inference problems in which nodes with unknown positions (agents) infer their positions based on distance measurements with respect to reference nodes (anchors) and other agents. In the indoor environment, the positions of agents can be constrained to finite geometric sets due to non-negative errors on the range estimates which arise from non-line-of-sight and multipath effects. First, we exploit the non-negativity of ranging errors in order to confine the positions of nodes to convex polygons. Subsequently, we exploit these polygons to relax the inference-based position estimation problems in terms of computational complexity. Using this two-stage approach, we show a tremendous reduction in terms of computational complexity, improvements in terms of localization accuracy, as well as the quicker convergence of the inference algorithm.

When time-based radio range measurements between network nodes are perturbed by a line-of-sight blocking obstacle, position estimation accuracy degrades significantly. The perturbation is caused by multipath propagation or excess delays since waves travel at slower speed while piercing the obstacles. Degradation of positioning accuracy results from position estimators not being aware of the line-of-sight blocking obstacle, i.e. the estimator assumes line-of-sight, but reality is non-line-of-sight. Hence the assumed model of the estimator differs from reality. In such a scenario, the model is said to be misspecified or mismatched. To assess the performance of position estimators operating under model misspecification, we employ the misspecified Cramér-Rao bound. We use the misspecified Cramér-Rao bound to predict the performance of a maximum likelihood position estimator under model mismatch. We numerically show a large increase of the mean squared error when the maximum likelihood estimator is misspecified. Based on our findings, we emphasize the importance of non-line-of-sight identification for designing position estimators.

We propose a novel message compression scheme for distributed nonparametric belief propagation in the context of cooperative localization. The messages to be exchanged in nonparametric belief propagation are given as particle representations. Communication constraints on the links make the transmission of entire particle representations prohibitive and require compressed (approximated) messages. We tackle the problem of compressing messages in an information-theoretic manner. For that reason, we define a relevant random variable and consider the mutual information shared with it for particle selection. We show that the shared mutual information depends on the choice of the particles. We propose a particle-based message compression algorithm that flexibly trades localization accuracy for communications, i.e. we incorporate a scalar parameter to adjust the degree of compression. We show that the proposed message compression method outperforms its parametrized counterpart in terms of localization accuracy for a well-chosen degree of compression, but comes at the expense of somewhat increased communications.

We propose a novel scheduler for an LTE-A network, which dynamically coordinates inter-cell interference for vulnerable users while limiting the effort of coordination. Vulnerable users are identified first, and then protected by assigning resources exclusively to them. Inter-cell interference coordination (ICIC) messages are transmitted to neighboring base stations (eNodeBs) via the X2 interface using LTE-typical constraints. The proposed scheduler and ICIC methods are compliant with the LTE and LTE-A standards. Closed-loop spatial multiplexing is used. The proposed scheduler is evaluated in a 2-tier heterogeneous network (HetNet). In comparison with the benchmark schedulers, simulation results for vulnerable users show a substantial gain in terms of throughput. In addition, the proposed scheduler guarantees a minimum data rate for a longer time.