Various indoor positioning methods have been developed to solve the “last mile on Earth”. Ultra-wideband positioning technology stands out among all indoor positioning methods due to its unique communication mechanism and has a broad application prospect. Under non-line-of-sight (NLOS) conditions, the accuracy of this positioning method is greatly affected. Unlike traditional inspection and rejection of NLOS signals, all base stations are involved in positioning to improve positioning accuracy. In this paper, a Long Short-Term Memory (LSTM) network is used while maximizing the use of positioning equipment. The LSTM network is applied to process the raw Channel Impulse Response (CIR) to calculate the ranging error, and combined with the improved positioning algorithm to improve the positioning accuracy. It has been verified that the accuracy of the predicted ranging error is up to centimeter level. Using this prediction for the positioning algorithm, the average positioning accuracy improved by about 62%.

Indoor fires cause huge casualties and economic losses worldwide. Thus, it is critical to quickly and accurately perceive the fire. In this work, an indoor fire perception algorithm based on multi-sensor fusion was proposed. Firstly, the sensor data features were fully extracted by improved temporal convolutional network (TCN). Then, the dimension of the extracted features was reduced by adaptive average pooling (AAP). Finally, the fire classification was realized by the support vector machine (SVM) classifier. Experimental results demonstrated that the proposed algorithm can improve accuracy of fire classification by more than 2.5% and detection speed by more than 15%, compared with TCN, back propagation (BP) neural network and long short-term memory (LSTM). In conclusion, the proposed algorithm can perceive the fire quickly and accurately, which is of great significance to improve the performance of the current fire prediction systems.

In this paper, we propose a multi-feature fusion network (MFF-Net) for a modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) monitoring scheme. The constellation map data used in this work comes from five modulation formats, namely 56 Gbit/s 4/8 phase shift keying (PSK) and 16/32/64 quadrature amplitude modulation (QAM). The constellation maps are input to one branch network of the MFF-Net, and then the constellation maps are processed by horizontal projection and used as input to another branch network as a way to fuse the two image features. The results show that the scheme achieves 100% MFI accuracy and 98.82% OSNR monitoring accuracy for the five modulation formats. In addition, the performance of MFF-Net and binarized convolutional neural network (B-CNN), visual geometry group network (VGG-Net), and traditional weighted multi-task learning (EW-MTL) are compared to present the superiority of the method. The effect of model structure on MFF-Net is also discussed. The robustness of the model is also evaluated for different transmission distances and bit rates.

This paper proposes a Kalman filter-based cascaded PI-(1+PI) controller, optimized using an Improved Spider Wasp Optimizer (ISWO), to address the challenges of USV heading control in dynamic marine environments. Traditional PID controllers struggle with nonlinearities and noise in USV systems while existing metaheuristic algorithms face limitations in balancing exploration and exploitation. To overcome these issues, the ISWO integrates dynamic adaptive grouping, perturbation dimension-symmetric distance optimization, and nonlinear time-varying weights, enhancing convergence speed and optimization accuracy. A transfer function model of the USV heading system is established using voyage data, with ISWO optimizing its parameters, achieving a 5.67% reduction in mean squared error (MSE) compared to the original Spider Wasp Optimizer and outperforming classical algorithms like Arithmetic Optimization Algorithm (AOA), Crayfish Optimization Algorithm (COA), and Marine Predators Algorithm (MPA). The proposed KF-PI(1+PI) controller incorporates a Kalman filter to suppress noise and a cascaded structure to improve gain and response speed, reducing integrated time absolute error (ITAE) by 84% relative to traditional PID controllers. The hardware-in-the-loop simulation experiments further validate the proposed controller’s robustness. The study demonstrates that ISWO-optimized control systems significantly enhance USV navigation precision and adaptability, offering a viable solution for autonomous marine operations.