Distance-based localization using Time-of-Arrival (ToA) estimation is fundamentally constrained by the accuracy of channel parameter tracking in non-stationary, high-mobility environments. In such scenarios, the estimation landscape becomes highly non-convex and multi-modal, rendering conventional gradient-driven trackers inadequate. This paper proposes a Gated Recurrent Unit (GRU)-enhanced Whale Optimization-based Tracking Algorithm (WoTA), a robust framework that synergistically fuses meta-heuristic global optimization with recursive filtering. Specifically, an Opposition-based Learning Whale Optimization Algorithm (OBL-WOA) is integrated to adaptively explore the complex cost function surface, effectively initializing the tracker and preventing local optima entrapment in multi-modal distributions. To overcome the limitations of manual parameter tuning in traditional WOA and Kalman Filters (KF), a GRU-based adaptive mechanism is introduced. This mechanism learns the temporal dependencies of optimization residuals to dynamically recalibrate the filter’s noise covariance and optimization hyperparameters, thereby enhancing resilience against model uncertainties. By coupling OBL-WOA’s global exploration with the KF’s exploitation of temporal kinematics, WoTA achieves smoothed and precise state transitions. Comprehensive simulations and real-world channel sounding measurements demonstrate that WoTA consistently outperforms state-of-the-art methods, such as SAGE and KEST. These results demonstrate that the proposed integration of heuristic exploration and recursive refinement offers a scalable and accurate framework for channel parameter tracking in dynamic multipath environments, thereby supporting reliable 6G wireless systems.

This paper investigates the delay-doppler characteristics of UAV A2G channels at 2.7 GHz, focusing on straight-line flight trajectories. Based on measured channel data, the doppler shift of the LoS path is estimated and verified, using UAV flight coordinate data. The normalized average doppler spectra under different trajectories are analyzed, and models for each channel tap are established. Comparative studies show that the Bell spectrum model outperforms the Jakes and Gaussian spectra in fitting the doppler characteristics, with lower RMSE. These findings provide theoretical support for channel modeling in 6G airborne networks, laying a foundation for improving wireless communication performance in UAV applications.

The rapid expansion of marine-related industries has intensified the need for robust maritime data transmission technologies. Traditionally, high-speed millimeter wave (mmWave) systems have been deemed unsuitable for marine environments due to significant propagation path losses (PLs). This study investigates the potential of marine evaporation ducts, a natural phenomena at the air-sea interface, to enhance mmWave frequency propagation by reducing signal attenuation. To examine the large-scale propagation characteristics of mmWave frequencies in environments featuring evaporation ducts, this research employed numerical simulations using ray tracing and parabolic equation methods. Additionally, a detailed field experiment was conducted to measure the PLs for a 22 GHz frequency over a 20 km shore-to-shore, over-the-horizon link with horizontal to horizontal polarization. The findings from both the numerical simulations and field tests reveal substantial multi-path effects caused by the evaporation duct phenomenon. These results indicate the necessity for orthogonal time frequency space technology to support high-speed, low-latency marine communications at mmWave frequencies.

Due to the flexibility and superior Line-of-Sight (LoS), Unmanned Aerial Vehicles (UAVs) have shown significant potential in Internet of Things (IoT) data collection. As the scale of IoT expands rapidly, higher demands are placed on energy efficiency and data transmission reliability. However, the limited battery life of UAVs restricts the application of a single UAV in large-scale, high-density wireless networks for data collection and data being sent to cloud for processing leads to poor Quality of Service (QoS) in traditional networks. To address these challenges, this paper aims to minimize UAV energy consumption while ensuring data collection reliability, proposing an energy-efficient data collection scheme in a cooperative multi-UAV scene. This scheme divides the non-convex problem into three subproblems for solution. First, to ensure the reliability of data transmission, introducing the guarantee of outage probability as a constraint, hovering altitude of the UAV is optimized to achieve the maximum coverage radius in the target area. Second, an Affinity Propagation (AP) clustering algorithm is introduced to partition the geographical area into the clusters with the minimum number which corresponds to the number of UAV movements. Finally, the set of horizontal position of the UAV hovering points can be optimized. And then, based on the three-dimensional (3D) coordinates, a hierarchical path planning algorithm for multiple UAVs is proposed which is formulated as a minimize maximum multiple traveling salesman problem (min-max MTSP) and solved effectively. Simulation results demonstrate that compared to existing methods, the proposed multi-UAV data collection scheme can ensure the reliability and decrease energy consumption.

Most focusing-based beamforming methods are devoted to solely focusing matrix designing, which aims to minimize the overall focusing error. However, these methods may suffer from performance degradation when steering vector (SV) error exists. To maximize the overall performance of focusing-based beamformer, this paper presents an adaptive focusing transformation-based beamforming algorithm using covariance matrix reconstruction. We first design a data-based focusing matrix of each frequency bin by assuming the Capon spectrum as the probability density function (PDF) of the incident signals. Then, the focused interference-plus-noise covariance matrix (INCM) can be reconstructed and the SV of desired signal is corrected. The above procedure not only decrease the focusing error based on the received signal, but also enhance the beamformer performance when there are SV mismatches, especially when the signal-to-noise ratio (SNR) is high.

Abstract Recently, the traffic application based on vision in the single traffic‐monitoring scene has been widely studied and developed. However, cross‐regional research is still in its infancy. In order to help solve the application of cross‐regional traffic surveillance scenarios, this paper proposes a more reliable and accurate road scene mosaic method under multi‐camera surveillance. The mosaic road panorama contains physical information, which can be used to achieve a cross‐regional measurement. It also lays the foundation for vehicle spatial location, vehicle speed and traffic incident detection across regions. First, the mapping relationship between the three‐dimensional sub‐world coordinates and their corresponding two‐dimensional image coordinates is established by camera calibration. Second, the projection transformation relationship between two cameras is established by two sub‐world coordinate systems and their common information. Finally, we use the proposed inverse projection idea and translation vector relationship to complete the mosaic of two traffic‐monitoring road scenes. The experimental results show that the camera calibration accuracy can reach more than 97% in a single scene. The measurement accuracy of the mosaic block is over 95%. The experimental results show that the proposed method has a higher accuracy, which has great value in related theoretical research and practical applications.

In this letter, a novel reconstruction-based adaptive beamformer is proposed, which uses linear prediction to generate virtual sensor data and extend array aperture. To overcome suppression failure of reconstruction-based adaptive beamformer, a double-side array extending algorithm is proposed for uniform linear array, where the virtual array data can be obtained by using the data from real array. Then, we estimate the spatial spectrum of extended array data with higher resolution by using a modified diagonal loading-type procedure, which is decided by the condition numbers of the sample covariance matrix (SCM) and the extended SCM, and the interference-plus-noise covariance matrix (INCM) of extended array is estimated. Without any optimization procedure, steering vector (SV) of extended array is corrected as the eigenvector corresponding to the dominant eigenvalue of the extended SCM. Numerical Simulations demonstrated that the proposed beamformer can use both deep nulls and low side-lobes to ensure better interference suppression capability to other beamformers in the case of sensor position errors.

In this paper, we propose a novel channel impulse response (CIR) clustering algorithm using a sparsity-based method, which exploits the feature of CIR that power of multipath component (MPC) is exponentially decreasing with increasing delay. We first use a sparsity-based optimization to recover CIRs, which can be well solved by using reweighted ℓ1minimization. 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. The proposed algorithm incorporates the physical behaviors of MPCs into the clustering framework and enables applications with no prior knowledge of the clusters, such as number and initial locations of clusters. The results in this paper can be used to parameterize the CIR model of radio channels.

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.

To investigate the broadband wave propagation channel for communication applications in harbor environments, the German Aerospace Center conducted channel sounder measurements where the transmit antenna was mounted on a ship and the receive antenna was located on land. Using this setup, measurements were performed for C-band at 5.2GHz with a broadband signal of 100MHz bandwidth. Path loss and shadow fading have been studied and are described in this contribution. Results show that the path loss for ship-to-land radio links within harbor environments depends on the geometry. Path loss exponents for non line-of-sight (NLoS) propagation scenarios are extracted with respect to the land-based antenna height. Furthermore, it has been found that there is no significant statistical difference for the shadow fading in line-of-sight and NLoS scenarios.

Time based positioning by terrestrial mobile radio as a complement to global navigation satellite systems has gained remarkable attention. As an essential tool to develop suitable algorithms for positioning with mobile radio signals, models of the wireless channel for this application are significant. However, there is still a lack of channel models suitable for positioning applications. In this paper we propose a geometry-based statistical time variant model to validate the accuracy of positioning algorithms. A statistically generated equivalent reflector is used to characterize each single path in the impulse response. Moreover, a model is proposed which accurately models the movement of each equivalent reflector. It is shown that the proposed channel model accurately fits with measurement data.

A general path loss model for in-room radio channels is proposed. The model is based on experimental observations of the behavior of the delay-power spectrum in closed rooms. In such a room, the early part of the spectrum observed at different positions typically consists of a dominant component (peak) that vanishes as the transmitter-receiver distance increases; the late part decays versus distance according to the same exponential law in delay regardless of the distance. These observations motivate the proposed model of the delay-power spectrum with an early dominant component and a reverberant component. The dominant component is modeled as a Dirac delta function weighted with a factor decaying according to an inverse distance power law (d-n). The reverberant component is an exponentially decaying function versus delay with distance-dependent onset. Its power decays exponentially with distance. The proposed model allows for the prediction of path loss, mean delay, and rms delay spread versus distance. We use measurements to validate the model. We observe good agreement of the model prediction for mean delay and rms delay spread.

Time Based (TB) localization in terrestrial communications mobile radio as a complementation to global navigation satellite systems has gained recently plenty of interests. As an essential tool to develop suitable algorithms for joint communications and localizations in mobile radio networks, the wireless channel model has a growing significance. For both communications and localizations, the time evolution of multipath components is essential especially for tracking applications. Moreover, the Non Line-of-Sight (NLoS) error due to an undetectable line-of-sight path introduces an additional range error for TB localizations, which needs to be taken into account in the channel model. In this paper we present a space-alternating generalized expectation-maximization based Kalman filter method to efficiently estimate and track the time-variant multipath components based on a channel measurement campaign. The modeling of time-variant multipath characteristics, in terms of NLoS error, variance of path number, and the life span, are presented in this paper as well.

Time Based (TB) localization by terrestrial cellular communications mobile radio as a complementation to Global Navigation Satellite Systems (GNSSs) has gained plenty of interests. Apart from multipath considered in standard communication channel models, the Non Line-of-Sight (NLoS) error due to an undetectable Line-of-Sight (LoS) path, defined as the additional propagation distance between the first detectable path and the Geometric LoS (GLoS), needs to be taken into account. In TB localizations, the range estimates from at least $3$ different links to Base Stations (BSs) are required to solve the $2$ dimensional navigation equation. In this paper we analyze the link-level NLoS errors based on a channel measurement campaign. It turns out to be, that the NLoS errors for difference links are i.i.d. processes.

The time based localization utilizing cellular communication networks has been investigated as a complementation to Global Navigation Satellite Systems (GNSS) for critical scenarios, like indoor or urban canyon areas. By suitable Hybrid Data Fusion (HDF) algorithms which combine the information from GNSS and terrestrial cellular networks, the estimated position accuracy can be improved. However, the wave propagation characteristics for joint GNSS and terrestrial mobile radio based localization as application has not been studied yet. Therefore, a measurement campaign for GNSS at 1.51 GHz and terrestrial radio at 5.2 GHz was performed. In this paper, an analysis of the outdoor to indoor channel for the joint localization as application is presented. It turns out to be that the Time of Arrival (ToA) bias, which is the difference between the geometric distance and the distance propagated by the first incoming wave, is depending on the elevation angle of incoming rays seen from the building to the transmitter. A comparison between two carrier frequencies is addressed.

Time Based (TB) localization in terrestrial cellular mobile radio as a complementation to Global Navigation Satellite Systems (GNSSs) has gained plenty of focuses. Due to absence of the Line-of Sight (LOS), Time of Arrival (ToA) of the First Path (FP) and/or corresponding Time Difference of Arrival (TDoA) are drifted from their true values. However, there are few analyses in terms of geolocations for current outdoor to indoor cellular channel models. In this paper ToA and TDoA error models based on the channel measurement are proposed, where both fit the Gaussian distribution well. Furthermore, with Monte Carlo simulation the positioning capabilities in a microcellular network scenario are investigated based on these models, where the TDoA based positioning promises a better solution compared to ToA.