Compared to broader physical activities, detecting nuanced respiratory movements poses a significant challenge in indoor health monitoring systems. While respiratory activity can be conceptualized as periodic chest movements akin to mechanical vibrations, uncontrollable environmental factors often introduce noise into detected radar signals. The clutter in the field of view, especially metallic objects, such as hospital steel beds, degrades the performance of radar physiological monitoring: 1) amplifying noise of multipath effects and 2) misleading the informative localization module. In this article, we propose a preprocessing scheme of Search-Voxel Ellipse Normalization for respiratory detection system, including an ellipse normalization method combined with the fitting-cost voxel selection policy, to improve the respiration detection performance using MIMO frequency modulated continuous wave radar. This article provides an in-depth assessment of the designed system, including a metal-insulated room test, involving ten participants in different postures. The results show notable performance improvements of our proposed ENDTW-MVMD method, especially in lowering the mean absolute error from the best state-of-the-art 0.93–0.75 bpm and stabilization in voxel selection. The proposed approach is thoroughly evaluated against established methods across various dimensions, such as voxel selection, independent performance, frequency estimation, and ablation studies.

This In the context of autonomous driving within vehicular networks, the accuracy of vehicle positioning is crucial for smooth operation. However, single navigation systems, such as satellite navigation and inertial navigation, cannot fully guarantee continuous high-precision positioning of vehicles. Therefore, achieving high-precision positioning through information collaboration between vehicles has become a primary approach. This paper proposes a large-scale vehicle cooperative positioning method based on neural networks. This method addresses the characteristics of vehicles freely clustering and dispersing during travel by introducing Principal Component Analysis (PCA) to process navigation information, reducing computational complexity. Additionally, it employs the Fireworks Neural Network method to rapidly integrate navigation information within the vehicular network, ensuring positioning accuracy and stability during vehicle operation. Compared with existing cooperative positioning methods, experimental results show that the proposed method has faster convergence speed and greater positioning stability.

This letter discusses the improvement of cooperative positioning frameworks for wide-area unmanned aerial vehicle (UAV) networks under the ranging measurement alone. The cooperative nodes are far apart in the wide-area UAV networks, which makes the angle measurement methods result in significant 3-D positioning errors. Therefore, only the ranging information between nodes is utilized to optimize the positioning results. To improve the accuracy and computation speed of the cooperative positioning algorithm, the ranging measurement-based manifold gradient fusion (RM-MGF) method is proposed. An error propagation model for cooperative nodes based on dilution of precision (DOP) is derived, making the error estimation more accurate. Furthermore, the Riemannian manifold gradient corresponding to the measurement network is applied to the improvement of positioning accuracy. The experiment results verify that the proposed algorithm has the best positioning accuracy and lower computational complexity.

This letter discusses the improvement of cooperative positioning method for distributed system under the condition of nonlinear measurement. In order to improve the accuracy and convergence speed of the cooperative positioning algorithm based on Bayesian filtering, a cooperative positioning algorithm utilizing Manifold Gradient Filtering (MGF) is proposed. The Information Geometry (IG) theory is applied to derive the manifold gradient in distributed cooperative filtering, which can analysis the geometric structure inherent in the distribution information of nonlinear measurement and make the fusion result more accurate. In addition, the proposed algorithm has fast convergence performance in the iterations. The simulation results demonstrate the accuracy and great performance of the proposed method.

Abstract Visual inertial odometry (VIO) would have an estimation drift problem in the process of long trajectory for indoor localization, especially in the absence of loop detection or in unknown complex scenes. To solve this problem, a low drift visual inertial odometry with ultra‐wideband (UWB) aided for indoor localization was proposed. Firstly, a single UWB anchor was dropped in an unknown position, and a cost function was formed by the position information output by VIO and the UWB ranging information to obtain the position of the anchor. Then, the single anchor position and the UWB ranging constraints were added to the tightly coupled visual inertial fusion algorithm framework, thereby improving the robustness of motion tracking and reducing the drift of the odometry. Finally, the effectiveness of the proposed method was verified in the actual indoor environment, and the experiment results demonstrated that, compared with state‐of‐the‐art localization methods, the positioning accuracy and robustness were improved significantly.

Exploiting a single‐antenna receiver to realise three‐dimensional (3D) positioning in a millimetre‐wave (mmWave) system is considered. The primary motivation is that the massive antenna arrays will be deployed in the fifth‐generation (5G) base stations shortly soon, which not only tremendously promote the data transmission rate, but also enable the user equipment to realise high‐precision positioning with a single antenna. Based on the sparsity of the mmWave channel, the tensor decomposition is proposed to be utilised as an effective mathematical tool to realise 3D positioning. Specifically, the authors model the received signals as a third‐order tensor for the inherent third‐order low‐rank tensor structure of the mmWave channel with a single‐antenna receiver and then, the positioning parameters (including the angles of departure and the time of arrival) are estimated from the corresponding factor matrices via CAMDECOMP/PARAFAC (CP) decomposition. Moreover, Cramér–Rao bounds (CRBs) on 3D position uncertainty are derived. Numerical results demonstrate that the proposed method based on CP decomposition realises nearly the same positioning accuracy as the state‐of‐the‐art compressed sensing‐based algorithm in the 5G mmWave systems with lower computation complexity, and the root mean square errors of the 3D positioning results obtained via the proposed approach are close to their CRBs.

Two‐way (on‐frequency) relaying using simple amplify‐and‐forward processing at the satellite isan emerging technology that allows doubling of system spectral efficiency forcertain networking applications. However, the satellite channel is non‐linearand compensation for the self‐interfering signal is more difficult than onlinear channels, especially when memory effects are recognised. In this paper, we model two‐user, asynchronous, bidirectional relaying for uncodedtransmission, and proposed a bidirectional satellite communication algorithmunder same frequency transmission. First, we employ a table addressed byside‐information and the symbol to be detected to model the action of thechannel. Then, we employ a linear adaptive canceler followed by a smaller tablelookup process to cancel the self‐interference and to make decisions. In thesimulation part, we focus in the results section is upon 16‐amplitude phaseshift keying (APSK), widely employed in the DVB‐S2 standard forbandwidth‐efficient operation. Compared with the existing bidirectionalsatellite communication algorithm, our proposed algorithm have the far lesscomplexity and training time at the similar performance.