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The popularity of next-generation networks has brought ubiquitous location-based demands. This article proposes a node deployment-aided intelligent optimization estimation (NDIOE) for target positioning in terms of node deployment, wireless ranging, calibration errors, and the algorithm itself. Since the optimized deployment of anchor nodes is the basis of high-accuracy positioning, we derive the characteristic matrix closely related to the upper bound of positioning error and map the signal domain to the location domain for a preliminary result solved by unconstrained optimization estimation. On this basis, the preliminary locations as the initial search values can be further refined for more accurate locations with the use of improved local particle swarm optimization in a narrowed searching area, whose adaptive fitness function, inertia weight factor, and mutation operation are designed to avoid falling into the local optimal. Aside from that, considering the high computational cost of solving extreme values through node traversal, we theoretically derive the theoretical minimum expression of any point based on Cramer–Rao lower bound (CRLB) as a benchmark, whose theoretical minimum value is $0.2^{2}$ in the fully connected network. Compared with the existing related algorithms, the proposed NDIOE algorithm is effectively validated under simulation and experimental platforms, as well as in complex environments with obstacles.

Position is one of the basic attributes of an object, which is one of the key technologies for its collaborative operation. As a distributed sensing method, wireless sensor networks (WSNs) have become a feasible solution especially in satellite signal denied environments. Considering that the node deployment is the basis of target positioning in WSNs, this paper first researches optimal deployment of wireless nodes, and then researches the optimal positioning of mobile targets. Based on the least squares equation, a feature matrix that can characterise the positioning error is derived so that the positioning error caused by wireless node deployment is minimised. Following that, the positioning results are refined by particle swarm optimisation, which makes the mobile target have a coarse to fine accuracy. The results indicate that the proposed algorithm can reduce the influence of network topology on positioning error, which is critical for some location-based applications.

Dear Editor, A global and local canonical correlation analysis (GLCCA) based on data-driven is presented for underwater positioning. Underwater positioning technology can help the underwater targets move predetermined destinations for specific tasks [1]. Since using different sensor, underwater positioning can be divided into three types: inertial navigation, hydroacoustic positioning and geophysical navigation. Underwater inertial navigation method based on the carried sensors has short-term high accuracy, but it is prone to accumulative errors over time and requires external correction [2]. Within the range of pre-deployed hydroacoustic arrays, hydroacoustic positioning method calculates the signal extracted from nodes, but may have fluctuating errors due to the complex hydroacoustic channels [3]. Geophysical positioning method mainly compares the collected information such as seabed terrain, underwater image, or gravity field with the prior knowledge in the reference database [4]. Different positioning methods have their unique merits as well as inherent drawbacks for different applications. How to combine multiple methods to enhance positioning performance has become the holy grail in the field of underwater positioning.

Aerogel-based composites, renowned for their three-dimensional (3D) network architecture, are gaining increasing attention as lightweight electromagnetic (EM) wave absorbers. However, attaining high reflection loss, broad effective absorption bandwidth (EAB), and ultrathin thickness concurrently presents a formidable challenge, owing to the stringent demands for precise structural regulation and incorporation of magnetic/dielectric multicomponents with synergistic loss mechanisms within the 3D networks. In this study, we successfully synthesized a 3D hierarchical porous Fe3O4/MoS2/rGO/Ti3C2TxMXene (FMGM) composite aerogel via directional freezing and subsequent heat treatment processes. Owing to their ingenious structure and multicomponent design, the FMGM aerogels, featured with abundant heterogeneous interface structure and magnetic/dielectric synergism, show exceptional impedance matching characteristics and diverse EM wave absorption mechanisms. After optimization, the prepared ultralight (6.4 mg cm–3) FMGM-2 aerogel exhibits outstanding EM wave absorption performance, achieving a minimal reflection loss of −66.92 dB at a thickness of 3.61 mm and an EAB of 6.08 GHz corresponding to the thickness of 2.3 mm, outperforming most of the previously reported aerogel-based absorbing materials. This research presents an effective strategy for fabricating lightweight, ultrathin, highly efficient, and broad band EM wave absorption materials.