This paper presents an algorithm termed as multiobjective dynamic rapidly exploring random (MOD-RRT*), which is suitable for robot navigation in unknown dynamic environment. The algorithm is composed of a path generation procedure and a path replanning one. First a modified RRT* is utilized to obtain an initial path, as well as generate a state tree structure as prior knowledge. Then a shortcuting method is given to optimize the initial path. On this basis, another method is designed to replan the path if current path is infeasible. The suggested approach can choose the best node among several candidates within a short time, where both path length and path smoothness are considered. Comparing with other static planning algorithms, MOD-RRT* can generate a higher quality initial path. Simulations on the dynamic environment are conducted to clarify the efficient performance of our algorithm in avoiding unknown obstacles. Furthermore, real applicative experiment further proves the effectiveness of our approach in practical applications.

This paper presents an efficient double-layer ant colony algorithm, called DL-ACO, for autonomous robot navigation. This DL-ACO consists of two ant colony algorithms which run independently and successively. First, a parallel elite ant colony optimization (PEACO) method is proposed to generate an initial collision-free path in a complex map, and then we apply a path improvement algorithm called turning point optimization algorithm (TPOA), in which the initial path is optimized in terms of length, smoothness and safety. Besides, a piecewise B-spline path smoother is presented for easier tracking control of the mobile robot. Our method is tested by simulations and compared with other path planning algorithms. The results show that our method can generate better collision-free path efficiently and consistently, which demonstrates the effectiveness of the proposed algorithm. Furthermore, its performance is validated by experiments in indoor and outdoor environments.

Illegally forged signals in automatic identification system (AIS) pose a threat to maritime traffic safety management. In this paper, a multi-level sparse representation based identification (MSRI) algorithm is proposed for specific emitter identification (SEI) in the AIS. The MSRI innovatively combines neural networks with sparse representation based classification (SRC). Channel attention mechanism is introduced to a multi-scale convolutional neural network (CNN) for extracting hidden features in the signal. These extracted features are divided into shallow and deep features according to the depth of the network layer they are extracted from. The original AIS signals and the two-level features are spliced together to form a multi-level dictionary. Subsequently, a sparse representation based identification is performed on the decorrelated multi-level dictionary using the principal components analysis (PCA) method. The proposed MSRI is evaluated on a dataset composed of real-world AIS signals, and compared with the state-of-the-art identification algorithms. The evaluation is based on several factors including computational complexity, number of training samples, and number of emitters. Numerical results indicate that the proposed algorithm can identify emitters with higher accuracy and requires lower training time compared to other methods. Given more than 15 training samples at each emitter, the MSRI can identify nine emitters with an accuracy higher than 90%.

The automatic identification system (AIS) is the automatic tracking system for automatic traffic control and collision avoidance services, which plays an important role in maritime traffic safety. However, it faces a possible security threat when the maritime mobile service identity (MMSI) that specifies the vessels’ identity in AIS is illegally counterfeited. To guarantee the communication security of AIS for preventing fraudulent devices, we design a novel lightweight Transformer-based network GLFormer for specific emitter identification (SEI) to provide an extra security layer for AIS terminal emitters. Concretely, the gated local attention unit (GLAU) and the gated sliding local attention unit (GSLAU) modules that combine a simplified gated attention unit (GAU) and a sliding local self-attention (SLA) are developed in GLFormer to extract the radio frequency fingerprint (RFF) features automatically from the raw in-phase signals. Especially, the simplified GAU focuses on more critical RFF features and filters out the irrelevant information from the raw signal to improve performance, which is also a single-head self-attention module with fewer parameters for lightweight. Meanwhile, the SLA limits self-attention operation to a window, introducing the inductive bias of local information to enhance performance further and reducing the quadratic computational complexity to linearity for efficiency. Experimental results demonstrate that the GLFormer achieves 96.31% and 89.38% identification accuracy in the constructed AIS transient and AIS steady-state datasets with 50 vessels, respectively. The 99.90% identification accuracy is achieved in the universal software radio peripheral (USRP) dataset with ten devices. It is not only better than the existing methods but requires much fewer parameters and lower computational complexity; besides, it is also suitable for working with long signal sequences.

The advent of modern communication systems has led to the widespread application of deep learning-based automatic modulation recognition (DL-AMR) in wireless communications. However, existing networks still cannot effectively capture the complex relationships between signals under low signal-to-noise (SNR) ratio conditions. This paper proposes an automatic modulation recognition (AMR) method using multi-modal hybrid neural networks, named IQFormer. It is based on I/Q signals and time-frequency (T-F) transform distribution matrix inputs. To capture the inherent connection between spatio-temporal and T-F features in cross-modal features, we design a Dynamic Fusion Embedding (DFE) module. Within this module, feature information from multiple modalities is dynamically aggregated during the embedding stage, resulting in semantically enriched token sequences. Moreover, we develop a staged Transformer block scheme that allows IQFormer to efficiently extract local and global features from embedded tokens at different scales using convolution and attention mechanisms. Experimental results on RadioML2016.10a, RadioML2016.10b and HisarMod2019.1 datasets demonstrate the superior performance of IQFormer compared to the state-of-the-art (SOTA) DL-AMR methods. Code is available at https://github.com/WestdoorSad/IQFormer.