Traditional clustering algorithms often face limitations in solving the problem of survivor clustering and rescue platform deployment in large-scale maritime search and rescue, due to their limited global exploration ability and tendency to be trapped in local optima. To address this problem, this paper proposes a step size adaptation Golden Eagle Optimization KMeans clustering algorithm (SAGeo-KMeans). The proposed algorithm combines the advantages of the global exploration capability of the Golden Eagle Optimization algorithm (GEO) and the local exploration capability of the KMeans algorithm through a novel step size adaptation factor. The factor considers the exploration completeness of the cluster centers, the spatial distribution of survivors, and the adaptive adjustment strategy of GEO, enabling dynamic adjustment of the search step size. Simulation results indicate that, compared with existing methods, the proposed algorithm can reduce the total rescue distance within a limited number of iterations, effectively improving search and rescue efficiency.

Conventional fast Fourier transform (FFT)-based frequency estimation in frequency-modulated continuous-wave (FMCW) radar altimetry suffers from limited resolution and noise sensitivity, which constrains ranging accuracy. To address this, this letter proposes a novel clustering-based frequency estimation algorithm leveraging multi-cycle triangular FMCW signals. The method increases signal redundancy without additional bandwidth by transmitting a multi-cycle triangular waveform. At the receiver, the beat signal undergoes envelope extraction, amplitude normalization, and a squaring operation to ensure phase coherence and enhance the fundamental component. A short-time Fourier transform (STFT) is then applied to obtain a time-frequency representation, followed by K-means clustering on the spectral maxima to extract the most stable and dominant frequency component. This process effectively suppresses spectral leakage, mitigates noise and outlier effects, and substantially improves estimation robustness. Simulation results demonstrate that the proposed algorithm achieves a relative altitude error below 1% at signal-to-noise ratios above 2 dB, significantly outperforming conventional FFT-based and STFT-averaging methods. Experimental validation using an FMCW radar confirms the superior accuracy and practical reliability of the approach, demonstrating its strong potential for high-precision radar altimetry in compact systems.

In large-scale urban environments, precise six-degree-of-freedom (6DOF) pose estimation is essential for vehicles and robots to perform autonomous driving and exploration, as well as to achieve high intelligence and full autonomy of Unmanned Aerial Vehicles (UAV). Achieving 6DOF pose estimation in Global Navigation Satellite System (GNSS)-denied environments is challenging. The performance of relative 6DOF localization systems based on Light Detection and Ranging (LiDAR), vision, and inertial data is easily affected by environmental conditions, leading to error accumulation and a significant decrease in estimation accuracy in complex environments. To address this issue, we propose a tightly coupled framework based on nonlinear optimization for vision, LiDAR, inertial, and GNSS raw data. In the experimental section, we validate the effectiveness of the proposed optimization factor model for GNSS data, LiDAR data, and visual data in improving position and orientation estimation accuracy through simulations. Additionally, we use real datasets to compare the proposed algorithm with several existing open-source programs in terms of computational efficiency, pose estimation accuracy, worst-case scenarios, and reliability. The experimental results show that, although the total processing time increases, the position estimation accuracy and orientation estimation accuracy of the proposed fusion algorithm improve by at least 58.0%. Overall, the proposed tightly-coupled algorithm outperforms the existing methods.

The fusion framework based on Light Detection and Ranging (LiDAR) and vision is susceptible to environmental constraints. Therefore, they are unable to adapt to complex and challenging environments. In response to this, this paper proposes a tightly coupled framework based on Global Navigation Satellite System (GNSS) data, which tightly couples GNSS, inertial, LiDAR and visual data. The experimental results demonstrate that, using publicly available challenging environment datasets, the proposed fusion system can achieve a 52.68% improvement in positioning accuracy compared to some existing fusion algorithms.

Spatiotemporal calibration is an essential problem in the fusion system with heterogeneous multi-source information. Therefore, a universal spatiotemporal calibration algorithm for heterogeneous information is much needed. This paper proposes a universal spatiotemporal calibration technique with the inertial sensor as the central coordinate system. Firstly, it employs a high-order spline interpolation method to transform the output data of the inertial sensor into a continuous form. Subsequently, the calibration model is established by combining the output data from other sensors. The paper provides detailed descriptions of the spatiotemporal calibration models for LiDAR (Light Detection and Ranging) data, and visual data, respectively. In the simulations, the proposed calibration models are applied to existing open-source programs using publicly available datasets. The results demonstrate that, with the adoption of the proposed calibration algorithm, the position estimation accuracy of the fusion system can be improved by 39%.

To achieve low-cost, low energy consumption green Internet of Things (IoT) communication and meet 360oarea full-coverage, we propose a novel simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted cognitive radio (CR)-non-orthogonal multiple access (NOMA) network. Specifically, the secondary transmitter serves as a relay of the primary network to forward the messages, and the secondary transmitter communicates with the user with the assistance of the STAR-RIS. To evaluate the performance of the considered network, we derive the outage probability (OP) for the users under the Nakagami-m fading channels. In addition, the asymptotic behavior at high signal-to-noise ratio (SNR) regions is analyzed. The following meaningful insights are obtained from the simulation experiments: 1) The OPs of users decrease continuously with the transmit power Ps, and increasing Psat high SNR is no longer effective for system reliability; 2) The increase in the components number of STAR-RIS has a positive impact on the reliability for the STAR-RIS assisted overlay CR-NOMA network and saturates after a certain value; 3) The scheme we considered has superior reliable performance by comparing with orthogonal multiple access.

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.

Aiming at the problem of positioning failure caused by the insufficient number of anchor nodes in wireless sensor network (WSN), this work introduces the Inertial Measurement Unit (IMU) into WSN and proposes a hybrid IMU/ultra-wideband (UWB) multisource fusion cooperative positioning algorithm for single-anchor networks. In this work, the UWB measurement information is used to construct the spatial relationship between neighbors, and the IMU measurement information is employed to obtain the position offset of the target in adjacent time. Benefiting from the IMU, multiple ranging information of the same target at adjacent times are combined in a time-space-changing manner. Then, the sum-product algorithm is proposed to derive the joint posterior distribution of nodes in sequential estimation. Furthermore, the target nodes position is estimated through peer-to-peer communication and measurement, and the cumulative error of IMU is corrected at the same time. Simulation results show that the proposed algorithm can solve the position problem in single-anchor networks.

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.

Summary Passive localization method has a wider applicable prospect as a rising localization resort with well flexibility. The acquisition of phase difference changing rate is a very practical problem in passive localization for the way how to get the phase difference, and a better error precision directly affects the accuracy of the localization. Firstly, based on the analysis of the positioning principle of phase difference changing rate, it studies the characteristics of phase difference changing rate and proposes a novel resolving phase ambiguity algorithm based on a double baselines system cosine theorem (DBSCT), which can eliminate the phase difference ambiguity efficiently. Secondly, it studies two typical methods to extract the high‐precision phase difference changing rate from the unambiguity phase difference, namely, difference algorithm and Kalman algorithm. Finally, it compares the Kalman algorithm with the difference algorithm through computer simulation. Simulation results show that the Kalman algorithm can efficiently smoothen the phase difference data and suppress the measurement noise to achieve a high positioning accuracy. Simulation results also show that with a certain phase difference measurement accuracy, the phase difference rate accuracy extracted by the Kalman algorithm is higher than that we require. Therefore, we can appropriately relax the phase difference measurement accuracy requirements to achieve the same positioning accuracy. So, it has a great application value.