Xinghe Chu

Achieving accurate and flexible localization in global navigation satellite system (GNSS)-denied underground spaces is critical for Internet of Things (IoT)-enabled logistics and personnel operations. To this end, this paper proposes a scalable multimodal localization framework requiring only a single ultra-wideband (UWB) transmitter connected to a distributed antenna system, avoiding the deployment of multiple additional anchors. An adjacency-masked, change-point-aware hidden Markov model (AC-HMM) is developed for region identification using only two-path UWB measurements, which avoids full channel impulse response (CIR) processing and reduces computational complexity. A multi-scale factor-graph maximum a posteriori inference method (MS-FGM) is then proposed for dynamic localization by fusing UWB and magnetic-field residuals with region and motion constraint factors. Multi-scale temporal aggregation is further introduced to mitigate motion-induced fluctuations and improve localization accuracy and stability. Experiments conducted along the roadways of an underground parking lot demonstrate an average region classification accuracy of 96.81% and a mean positioning error of 0.88 m, outperforming existing methods by up to 42.19%.

To achieve seamless and continuous positioning for smartphones in tunnels, where the global navigation satellite system (GNSS) signals are unavailable, this article proposes a new method that leverages the existing leaky coaxial cable (LCX) infrastructure from public land mobile networks to introduce the GNSS signals directly from outside the tunnels. This approach introduces three key innovations. First, a continuous hybrid GNSS-LCX channel is proposed which uses a waveguide-to-wireless model to provide continuous GNSS signal coverage via existing 5G LCX without requiring dedicated hardware. Based on this model, a bidirectional clock bias cancellation (BCBC) mechanism is designed for GNSS signals inside the tunnel. This method establishes a quantitative mapping between the position in the tunnel and the pseudorange observations incorporating clock bias, enabling the residual latency from smartphones and wired transmission in the tunnel to be modeled as a function of signal propagation distance and user tracks within the tunnel environment. Furthermore, a 5G-enhanced factor graph optimization (FGO) method is proposed, which integrates 5G measurements and tunnel topology to suppress positioning fluctuations caused by multipath effects in tunnels, while mitigating GNSS positioning latency and ambiguity induced by 5G cell handovers. Field tests in a 150-m tunnel show 1.21 m median accuracy, achieving 46% higher accuracy than fingerprinting and $3.2\times $ faster convergence than conventional methods. This solution reuses 5G-LCX infrastructure for cost-effective and consistent tunnel positioning that bridges the gap between open-sky and underground tunnel environments.

Wireless indoor positioning systems are challenged by the reliance on densely deployed hardware and exhaustive site surveys, leading to elevated deployment and maintenance costs that limit scalability. This paper introduces a novel positioning framework that enhances the existing In-Building Wireless (IBW) infrastructure by retransmitting Global Navigation Satellite System (GNSS) signals. Pseudorange residuals extracted from raw GNSS measurements, when mapped against known cable lengths, facilitate anchor identification and precise ranging. In parallel, directional and inertial measurements are derived from the channel state information (CSI) of cellular reference signals. Building upon these observations, we develop a Hybrid Adaptive Filter-Graph Fusion (HAF-GF) algorithm for high-precision positioning, wherein the adaptive filter modulates observation noise based on Line-of-Sight (LoS) conditions, while a factor graph optimization over multiple positional constraints ensures global consistency and accelerates convergence. Ray tracing-based simulations in a complex office environment validate the efficacy of the proposed approach, demonstrating a 30% improvement in positioning accuracy and at least a threefold increase in deployment efficiency compared to conventional methods.

The 4G/5G era in the past decades has witnessed the vigorous development of Hybrid Analog and Digital Beamforming (HBF) technologies in the field of communications under cellular Multiple Input Multiple Output (MIMO) systems. As an evolution, the B5G/6G has strong visions of high-accurate positioning capabilities other than the communication quality, thus a beam alignment method towards positioning enhancement is also urgently desired in cellular systems. To this end, this paper proposed a HBF method for positioning enhancement in cellular MIMO systems. We first derive the Fisher Information for multiple-path assisted positioning as the performance criterion of positioning under a wideband channel with both precoder and combiner considered. Then a HBF strategy is proposed to optimize such criterion over multiple resources involving the transmitting power, beam and frequency dimensions, which is referred to as Sensing Beamforming . Furthermore, a Newton based heuristic method is proposed for the estimation of sensing elements (e.g. angle of arrival) from multiple paths, and the positioning results are obtained by a proposed multiple-path assisted positioning method considering the multiple path clutters in the environment. The results indicate that the proposed method can enhance the positioning performance with the accurate estimation of sensing elements.

Based on the Richardson and Urbanke (RU) algorithm, the widely used quasi-cyclic low-density parity-check (QC-LDPC) code encodes the parity check matrix (PCM) in blocks, making hardware implementation possible. However, in a QC-LDPC encoding system with multiple expansion factors, storing information bits in excessive register bit widths would reduce the flexibility and throughput of the encoder when the RU algorithm is used. Therefore, this article proposes a novel architecture to reduce the complexity of the encoder by decreasing the number of expansion factors. This architecture optimizes the storage structure of the PCM and the pipeline structure of the encoding core, which obviously improves the flexibility and throughput of the encoder. In addition, this article presents two algorithms to optimize the pipeline structure, decreasing the latency for information bits to enter the encoder and ensuring that the pipeline of the encoding core would not stall. Moreover, the proposed architecture can be applied to encoding systems with multiple expansion factors, such as 5G and IEEE 802.11, and it has been verified on field-programmable gate array (FPGA). Compared with the most advanced solution, the proposed encoder achieves a 77 $\rm \%$ increase in resource utilization. As a case study, this encoder improves the performance of the soft base station by 2.59 times.

This paper aims to solve the trajectory tracking control problem for an autonomous vehicle based on reinforcement learning methods. Existing reinforcement learning approaches have found limited successful applications on safety-critical tasks in the real world mainly due to two challenges: 1) sim-to-real transfer; 2) closed-loop stability and safety concern. In this paper, we propose an actor-critic-style framework SRL-TR $^2$ , in which the RL-based TRajectory TRackers are trained under the safety constraints and then deployed to a full-size vehicle as the lateral controller. To improve the generalization ability, we adopt a light-weight adapter State and Action Space Alignment (SASA) to establish mapping relations between the simulation and reality. To address the safety concern, we leverage an expert strategy to take over the control when the safety constraints are not satisfied. Hence, we conduct safe explorations during the training process and improve the stability of the policy. The experiments show that our agents can achieve one-shot transfer across simulation scenarios and unseen realistic scenarios, finishing the field tests with average running time less than 10 ms/step and average lateral error less than 0.1 m under the speed ranging from 12 km/h to 18 km/h. A video of the field tests is available at https://youtu.be/pjWcN_fV24g.

This paper addresses high-resolution vehicle positioning and tracking. In recent work, it was shown that a fleet of independent but neighboring vehicles can cooperate for the task of localization by capitalizing on the existence of common surrounding reflectors, using the concept of Team Channel-SLAM. This approach exploits an initial (e.g. GPS-based) vehicle position information and allows subsequent tracking of vehicles by exploiting the shared nature of virtual transmitters associated to the reflecting surfaces. In this paper, we show that the localization can be greatly enhanced by joint sensing and mapping of reflecting surfaces. To this end, we propose a combined approach coined Team Channel-SLAM Evolution (TCSE) which exploits the intertwined relation between (i) the position of virtual transmitters, (ii) the shape of reflecting surfaces, and (iii) the paths described by the radio propagation rays, in order to achieve high-resolution vehicle localization. Overall, TCSE yields a complete picture of the trajectories followed by dominant paths together with a mapping of reflecting surfaces. While joint localization and mapping is a well researched topic within robotics using inputs such as radar and vision, this paper is first to demonstrate such an approach within mobile networking framework based on radio data.

This paper addresses vehicle positioning, a topic whose importance has risen dramatically in the context of future autonomous driving systems. While classical methods that use GPS and/or beacon signals from network infrastructure for triangulation tend to be sensitive to multi-paths and signal obstruction, our method exhibits robustness with respect to such phenomena. Our approach builds on the recently proposed Channel-SLAM method which first enabled leveraging of multi-path so as to improve (single) vehicle positioning. Here, we propose a cooperative mapping approach which builds upon the Channel-SLAM concept, referred to here as Team Channel-SLAM. Team Channel-SLAM not only exploits the stationary nature of many reflecting objects around the vehicle, but also capitalizes on the multi-vehicle nature of road traffic. The key intuition behind our method is the exploitation for the first time of the correlation between reflectors around multiple neighboring vehicles. An algorithm is derived for reflector selection and estimation, combined with a team particle filter (TPF) so as to achieve high precision simultaneous multiple vehicle positioning. We obtain large improvement over the single-vehicle positioning scenario, with gains being already noticeable for moderate vehicle densities, such as over 40 % improvement for a vehicle density as low as 4 vehicles in 132 meters' length road.

Connected autonomous driving is considered as a potential way to improve traffic safety. However, when positioning information is delivered through network, the network time delay could result in additional positioning errors, which has been proved by the experiment done in this paper in XuChang automatic driving center that the influence of one-way time delay will lead to meter-scale positioning error. In this paper, we propose a positioning calibration mechanism for connected autonomous vehicles called PCM. PCM firstly estimates the network time delay and then saves the calibrated location information in the MEC server to reduce the positioning error caused by network time delay. Experiments in FangShan and XuChang automatic driving centers verify the performance of PCM, and the result shows that PCM can reduce the positioning error to centimeter-level. For example, under 100ms time delay, the average positioning error without PCM is 119.67cm, while the average positioning error with PCM is 3.03cm. Experimental results show that PCM can effectively reduce the positioning error caused by a one-way time delay to centimeter-level, which is strictly required for positioning autonomous vehicles in automatic driving.