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.

In dynamic channel sounder measurements with a moving receiver and/or moving transmitter, propagation paths' parameters show a time-variant behavior. This paper describes a novel algorithm which is able to both detect individual propagation paths and track how these paths evolve with time. The proposed algorithm is capable of estimating the paths' parameters and outperforms the standard snapshot-based algorithm in cases where low accuracy due to resolution limitations in the delay or angular domain is observed. An example based on channel sounder measurements with a moving receiver is given, showing that the algorithm is able to capture the dynamic behavior of the propagation channel.

This work presents an overview of the radio interface of VHF Data Exchange System (VDES), which is currently on its way to become an ITU standard supported by International Association of Marine Aids to Navigation and Lighthouse Authorities. VDES includes the already existing collision avoidance tracking system Automatic Identification System and the messaging system Application Specific Messages. Additionally, a new third component for digital maritime communications of any kind, named VHF Data Exchange is included. On the one hand, there is a traditional terrestrial component, on the other hand, a satellite communication link is also envisioned partly by the same spectrum usage. In this article, focus is given towards the technical design aspects and challenges of this hybrid communications transmission scheme.

Radio channel modeling has been an important research topic, as the analysis and evaluation of any wireless communication system requires a reliable model of the channel impulse response (CIR). The classical work by Saleh and Valenzuela and many recent measurements show that multipath component (MPC) arrivals in CIRs appear at the receiver in clusters. To parameterize the CIR model, the first step is to identify clusters in CIRs, and a clustering algorithm is thus needed. However, the main weakness of the existing clustering algorithms is that the specific model for the cluster shape is not fully taken into account in the clustering algorithm, which leads to erroneous clustering and reduced performance. In this paper, we propose a novel CIR clustering algorithm using a sparsity-based method, which exploits the feature of the Saleh-Valenzuela (SV) model that the power of the MPCs is exponentially decreasing with increasing delay. We first use a sparsity-based optimization to recover CIRs, which can be well solved using reweighted ℓ ₁ minimization. Then, a heuristic approach is provided to identify clusters in the recovered CIRs, which leads to improved clustering accuracy in comparison to identifying clusters directly in the raw CIRs. Finally, a clustering enhancement approach, which employs the goodness-of-fit (GoS) test to evaluate clustering accuracy, is used to further improve the performance. The proposed algorithm incorporates the anticipated behaviors of clusters into the clustering framework and enables applications with no prior knowledge of the clusters, such as number and initial locations of clusters. Measurements validate the proposed algorithm, and comparisons with other algorithms show that the proposed algorithm has the best performance and a fairly low computational complexity.

Fixed-wing unmanned aerial vehicle (UAV) is widely considered as a vital candidate of aerial base station in beyond 5th-Generation (B5G) system due to its longer flight endurance and higher cruise altitude. Investigating and modeling the air-to-ground (A2G) wireless channel for fixed-wing UAV can better promote its development in various applications. In this paper, we present a wideband A2G channel measurement campaign for fixed-wing UAV with maximum altitude of 700 m. A comprehensive investigation of channel fading characteristics and channel modeling include path loss, shadow fading, spatial correlation, small-scale fading are provided. Particularly, unlike terrestrial channel a strong dependence of shadow fading and small-scale fading on UAV altitude is found. We propose to adopt Gamma distribution for modeling the shadow fading effect that has similar fitting performance with traditional Log-normal distribution but is more robust for theoretical analysis. Spatial correlation characteristics and modeling are presented. Results reveal that the decorrelation distance of shadow fading increases with the UAV altitude. We propose to use a model combining the exponential and sinusoidal functions that fit well to the autocorrelation function for shadow fading. Further, by using Kolmogorov-Smirnov (KS) test, Cramer-von Mises (CVM) test and Akaike information criterion (AIC) methods the Rician distribution is found to be the best candidate for modeling small-scale fading for UAV with altitudes above 300 m. The Rician K-factor is found to be strongly depending not only on the direct-distance but also on the UAV altitude. We finally propose a full dimension empirical K-factor prediction model.

Due to low cost and high operability, unmanned aerial vehicle(UAV) becomes a key component in future intelligent transportation system(ITS) for various use cases. Air-to-ground(A2G) communication is a fundamental technology for integration of UAV in the ITS, where accurate analysis and modeling of UAV A2G wireless channel are of great importance for communication algorithm development. In this paper, we present a comprehensive analysis and modeling of low altitude UAV A2G channel at 2.4 and 5.9 GHz based on multi-link channel measurement campaign. Based on the measured data, the value of path loss as a function of the elevation angle and distance is proposed. Further, the shadow fading(SF) is analyzed for different frequency bands and the spatial correlation is evaluated. Particularly, it is found that the influence of UAV altitude on shadowing is significant and should be considered when developing A2G communication system.