This paper offers a solution to challenge navigation in the indoor environment by making use of the existing infrastructure. Estimating pedestrian trajectory using pedestrian dead reckoning (PDR) and WiFi is a very popular technique. However, cumulative errors and mismatching are major problems in PDR and WiFi fingerprint matching, respectively. PDR and pedestrian heading are used as the state transition equation, and the step length and WiFi matching results are used as observation equations. A federated particle filter (FPF) based on the principle of information sharing is proposed to fusion PDR and WiFi, which improves pedestrian navigation accuracy. The experimental results show that the average positioning accuracy is 0.94 m and 1.5 m, respectively.

Obstacles on rail transit significantly compromise operational safety, particularly under dense fog conditions. To address missed and false detections in traditional rail transit detection methods, this paper proposes a multi-scale adaptive YOLO (MSA-YOLO) algorithm. The algorithm incorporates six filters: defog, white balance, gamma, contrast, tone, and sharpen, to remove fog and enhance image quality. However, determining the hyperparameters of these filters is challenging. We employ a multi-scale adaptive module to optimize filter hyperparameters, enhancing fog removal and image quality. Subsequently, YOLO is utilized to detect obstacles on rail transit tracks. The experimental results are encouraging, demonstrating the effectiveness of our proposed method in foggy scenarios.

The use of dead reckoning and fingerprint matching for navigation is a widespread technical method. However, fingerprint mismatching and low fusion accuracy are prevalent issues in indoor navigation systems. This work presents an improved dynamic time warping and a chicken particle filter to handle these two challenges. To generate the Horizontal and Vertical (HV) fingerprint, the pitch and roll are employed instead of the original fingerprint intensity to extract the horizontal and vertical components of the magnetic field fingerprint. Derivative dynamic time warping employs the HV fingerprint in its derivative form, which receives higher-level features because of the consideration of fingerprint shape information. Chicken Swarm Optimization (CSO) is used to enhance particle weights, which minimizes position error to tackle the particle impoverishment problem for a fusion navigation system. The results of the experiments suggest that the enhanced algorithm can improve indoor navigation accuracy significantly.

WiFi fingerprint-based positioning is a method for indoor localization with the advent of widespread deployment of WiFi and the Internet of Things. However, single WiFi fingerprint positioning has the problems of mismatch, unstable signal strength and limited accuracy. Aiming to address these issues, this paper proposes the fusion algorithm combining WiFi and pedestrian dead reckoning (PDR). Firstly, the particle swarm optimization (PSO) model is utilized to optimize the weighted k-nearest neighbors (WKNN) in the WiFi part. Additionally, the artemisinin optimization (AO) algorithm is used to optimize the particle filter (PF) to improve the fusion effect of the WiFi and PDR. Finally, to thoroughly validate the localization performance of the proposed algorithm, we designed experiments involving two scenarios with four smartphone gestures: calling, dangling, handheld, and pocketed. The experimental results unequivocally indicate that the positioning error of AO-PSO-PF algorithm is lower than that of other algorithms including PDR, WiFi, PF, APF, and FPF. The average positioning errors for the two experiments are 0.95 m and 1.42 m, respectively.

With the application of the random sampling method in the down-sampling of point clouds data, the processing speed of point clouds has been greatly improved. However, the utilization of semantic information is still insufficient. To address this problem, we propose a point cloud semantic segmentation network called MFFRand (Multi-Scale Feature Fusion Based on RandLA-Net). Based on RandLA-Net, a multi-scale feature fusion module is developed, which is stacked by encoder-decoders with different depths. The feature maps extracted by the multi-scale feature fusion module are continuously concatenated and fused. Furthermore, for the network to be trained better, the multi-loss supervision module is proposed, which could strengthen the control of the training process of the local structure by adding sub-losses in the end of different decoder structures. Moreover, the trained MFFRand network could be connected to the inference network by different decoder terminals separately, which could achieve the inference of different depths of the network. Compared to RandLA-Net, MFFRand has improved mIoU on both S3DIS and Semantic3D datasets, reaching 71.1% and 74.8%, respectively. Extensive experimental results on the point cloud dataset demonstrate the effectiveness of our method.

Object detection in UAV aerial imagery plays a vital role in applications such as traffic surveillance, urban management, and low-altitude inspection. However, aerial images typically present challenges including small object scales, dense distributions, severe occlusion, and cluttered backgrounds. Existing YOLO-series detectors still exhibit limitations in small-object feature representation, multi-scale contextual modeling, and downsampling detail preservation. To address these issues, this paper proposes MD-YOLO, an improved object detection model tailored for UAV scenarios, built upon the YOLO26 baseline. MD-YOLO incorporates three lightweight modules—IMO, DS-SPPF, and HPConv to optimize backbone feature extraction, multi-scale contextual aggregation, and Neck downsampling, respectively, thereby enhancing the model’s detection capability for small objects in complex UAV scenarios. Experimental results demonstrate that, compared with the baseline model, MD-YOLO achieves improvements of 3.9, 3.2, 2.9, and 3.1 percentage points in Precision, Recall, mAP50, and mAP50-95 on the VisDrone-2019 dataset, while maintaining a parameter count of only 9.3 M, thereby striking a favorable balance between accuracy and complexity. Independent evaluations on the UAVDT and NWPU VHR-10 datasets further confirm the consistent effectiveness of the model across diverse UAV imaging conditions, with mAP50 improvements of 8.3 and 1.4 percentage points, respectively.