The essence of human-like trajectory learning is to construct correspondences between scene elements and temporal trajectory points. Extracting key scene features and setting proper guidance during the learning process are crucial to improving the accuracy of human-like trajectory learning. Therefore, this paper proposes a graph feature extraction method for unstructured scene elements combined with a learning-based two-stage trajectory planner for human-like trajectory generation. The construction of the graph structure considers environmental, trajectory, and waypoint features, with environmental features specifically constructed through pixel clustering and motion compensation to enhance efficiency. In the first stage of the dual-phase trajectory planning, feature extraction is performed using Spatial-Temporal Graph Convo-lutional Networks (ST-GCN), followed by proposal trajectory generation with a sequence to sequence(Seq2Seq) network. In the second stage, the proposed trajectory from the first stage serves as the input, with predicted goal points obtained through the Multilayer Perceptron(MLP) network. The final trajectory is then generated by fusing graph and guidance features. The results demonstrate that the proposed scene graph structure effectively reduces the complexity of the learning network, thereby improving algorithm efficiency. Additionally, heatmap-guided features, jointly generated with the learned predicted goal points and the regularization method, effectively guide trajectory generation and improve the accuracy of human-like trajectory generation.

Amidst the rapid advancement of Internet of Things (IoT) technology, achieving precise indoor localization has emerged as a pivotal research area. Localization algorithms relying on Radio Frequency Identification (RFID) received signal strength indicator (RSSI) have gained widespread adoption in numerous indoor positioning systems due to their straightforward implementation and cost-effectiveness. However, in indoor settings, challenges like building obstructions and multipath effects often lead to signal reception failures by RFID antennas, consequently compromising the reliability of positioning outcomes. Recent research has approached indoor localization as a regression problem, employing deep learning models for analysis and prediction. But most current indoor localization models primarily focus on either spatial or temporal features within RSSI data, leading to suboptimal localization outcomes. To tackle these challenges, this paper proposes an enhanced methodology that leverages Generative Adversarial Networks (GAN) to impute missing RSSI data. Additionally, Convolutional Neural Networks (CNN) are utilized to extract spatial domain features, while Long Short-Term Memory Networks (LSTM) are employed for extracting temporal domain features. Ultimately, this paper designs a novel model, GCLA, which integrates an Attention mechanism with a location coding strategy to fuse features for precise location prediction. Experimental results show that the proposed GCLA model can obtain stable localization results after a short training on a small number of datasets.

Nowadays, IoT technology is developing rapidly and RFID (Radio Frequency Identification) based indoor positioning problems can be performed using deep learning and intelligent optimization algorithms. Deep models can analyze and predict the localization problem as a regression problem to achieve high accuracy positioning. Meanwhile, to ensure the accuracy of the model, we need to find excellent hyperparameters, which requires the support of optimization algorithms, but existing optimization algorithms do not allow flexible adaptation according to the optimization phase and there is room for improvement. In this paper, we propose a deep model, called CTT, and a multi-objective evolutionary algorithm (MOEA) based on a neighborhood adaptive adjustment strategy, called MOEA-NAAS. The experimental results show that CTT optimized by the NAAS algorithm is significantly more accurate and stable in the localization problem, with significant improvements in the three main metrics, proving the usability of the optimization algorithm. At the same time, the localization effect of the CTT also shows obvious advantages. In the future, the optimized algorithm can be combined with other deep models and widely used in various high-precision indoor positioning.

Wireless sensing techniques for Human Activity Recognition (HAR) have been widely studied in recent years.At present, the research on HAR based on Radio Frequency Identification (RFID) is changing from the tag attachment method to the tag non-attachment method.Affected by multipath, the current solutions in tag non-attachment scenarios mainly focus on singleobject activity recognition, which is not suitable for multi-object scenarios.To address these issues, we propose DeepMultiple, a novel tag non-attachment activity recognition model for multiobject.The model first preprocesses the raw signal with filter and phase calibration, then it applies dilated convolution in the frequency domain to extract multi-object activity features, finally ProbSparse is used to optimize the vanilla Transformer-based Encoder to enhance the activity recognition ability.We deployed a single reader and antenna for multi-object activity tracking during the experiments to reduce deployment difficulties.Extensive experimental results show that DeepMultiple can recognize ten types of multi-object activities with 98.12% precision under different challenging settings, which has excellent performance compared with several state-of-the-art methods.

Wireless sensing techniques for Human Activity Recognition (HAR) have been widely studied in recent years. At present, research on HAR based on Radio Frequency Identification (RFID) is changing from the tag attachment method to the tag non-attachment method. Affected by multipath, the current solutions in tag non-attachment scenarios mainly focus on single-object activity recognition, which is not suitable for multi-object scenarios. To address these issues, we propose STMultiple, a novel tag non-attachment activity recognition model for multi-object. The model first preprocesses the raw signal with filter and phase calibration, then it applies dilated convolution in the frequency domain to extract multi-object activity features, finally the feature pyramid structure and ProbSparse are used to optimize the vanilla Transformer-Encoder to enhance the activity recognition ability. Extensive experiments show that STMultiple can achieve recognition accuracy of up to 97.93% and down to about 90% in challenging environments ranging from two to five users, which has excellent performance compared to several state-of-the-art methods.