This paper describes an algorithm that exploits multipath propagation for position estimation of mobile receivers. We apply a novel algorithm based on recursive Bayesian filtering, named Channel-SLAM. This approach treats multipath components as signals emitted from virtual transmitters, which are time synchronized to the physical transmitter and static in their positions. Contrary to other approaches, Channel-SLAM considers also paths occurring due to multiple numbers of reflections or scattering as well as the combination. Hence, each received multipath component increases the number of transmitters resulting in a more accurate position estimate or enabling positioning when the number of physical transmitters is insufficient. Channel-SLAM estimates the receiver position and the positions of the virtual transmitters simultaneously; hence, the approach does not require any prior information, such as a room-layout or a database for fingerprinting. The only prior knowledge needed is the physical transmitter position as well as the initial receiver position and moving direction. Based on simulations, the position precision of Channel-SLAM is evaluated by a comparison to simplified algorithms and to the posterior Cramér-Rao lower bound. Furthermore, this paper shows the performance of Channel-SLAM based on measurements in an indoor scenario with only a single physical transmitter.

A multitude of autonomous robotic platforms collectively organized as a swarm attracts increasing attention for remote sensing and exploration tasks. A navigation system is essential for the swarm to collectively localize itself as well as external sources. In this article, we propose a self-aware swarm navigation system that is conscious of the causality between its position and the localization uncertainty. This knowledge allows the swarm to move in a way to not only account for external mission objectives but also enhance position information. Position information for classical navigation systems has already been studied with the Fisher information (FI) and Bayesian information (BI) theories. We show how to extend these theories to a self-aware swarm navigation system, particularly emphasizing the collective performance. In this respect, fundamental limits and geometric interpretations of localization with generic observation models are discussed. We further propose a general concept of FI and BI based information seeking swarm control. The weighted position Cramér-Rao bound (CRB) and posterior CRB (PCRB) are employed flexibly as either a control cost function or constraints according to different mission criteria. As a result, the swarm actively adapts its position to enrich position information with different emerging collective behaviors. The proposed concept is illustrated by a case study of a swarm mission for gas exploration on Mars.

Traditional network localization algorithms contain ranging and localization steps, which have systematic disadvantages. We propose an algorithm dubbed direct particle filter based distributed network localization (DiPNet). A node's location is directly estimated from the received signals, incorporating location uncertainty of neighboring nodes. The propagation effects on DiPNet become insignificant for dense networks, due to the massive-link collective physical layer processing. DiPNet achieves a near-optimal performance with low complexity, which is particularly attractive for realtime dense-network localization

A multimode antenna (MMA) can be an interesting alternative to a conventional phased antenna array for direction-of-arrival (DoA) estimation. By MMA, we mean a single physical radiator with multiple ports, which excite different characteristic modes. In contrast to phased arrays, a closed-form mathematical model of the antenna response, such as a steering vector, is not straightforward to define for MMAs. Instead, one has to rely on calibration measurement or electromagnetic field (EMF) simulation data, which is discrete. To perform DoA estimation, an array interpolation technique (AIT) and wavefield modeling (WM) are suggested as methods with inherent interpolation capabilities, fully taking antenna nonidealities like mutual coupling into account. We present a noncoherent DoA estimator for low-cost receivers and show how coherent DoA estimation and joint DoA and polarization estimation can be performed with MMAs. Utilizing these methods, we assess the DoA estimation performance of an MMA prototype in simulations for both the 2-D and 3-D cases. The results show that WM outperforms AIT for high signal-to-noise ratio. The coherent estimation is superior to noncoherent, especially in 3-D, because noncoherent suffers from estimation ambiguities. In conclusion, the DoA estimation with a single MMA is feasible and accurate.

Array processing is a key technology for emerging mobile networks, especially in short to moderate range and LOS scenarios. In these scenarios, the incoming wavefront can be modeled by a spherical wave. The wavefront curvature, i.e. Curvature of Arrival (CoA), contains position information of the transmitter and is observable by an antenna array potentially asynchronous and non-coherent to the transmitter. We derive a simplified expression of the spherical wave positioning (SWP) Cramér-Rao bound (CRB) for arbitrary centro-symmetric arrays (CSAs), which provides a geometrical inference about the achievable performance. Additionally, a low complexity CoA positioning algorithm is proposed. In contrast to conventional methods, the proposed algorithm requires neither multiple anchors nor coordination between devices. It also outperforms the Fresnel approximation based SWP algorithms by overcoming the model mismatch. Therefore, the proposed CoA positioning algorithm is promising for precise positioning in future mobile networks.