Cognitive radio (CR), as an emerging technology to improve the utilization of radio spectrum, the fundamental of CR technology is spectrum sensing, due to the detection performance being affected by various factorsïĳŇ spectrum sensing is challenging to achieve accurately.In recent years, many spectrum sensing algorithms have been proposed, such as energy detection algorithm, matched filter detection algorithm, cyclic stationary detection algorithm, etc.However, these algorithms are model-driven and require certain prior information.If the model assumptions are inaccurate or the prior information is challenging to obtain, the algorithms' detection performance will be degraded.The development of artificial intelligence technology and deep learning provides a new way to realize spectrum sensing.In this paper, we design a cooperative spectrum sensing model based on the parallel connection of convolutional neural network (CNN) and long-short-term memory (LSTM), which makes full use of the complementary feature extraction capabilities of CNN and LSTM networks.Among them, CNN is used to extract hidden spatial features, and LSTM network is used to extract time features.Both CNN and LSTM can process the original dataset directly avoiding information feature loss when the network is connected serially.Experimental result shows that the detection performance of the proposed algorithm outperforms the conventional cooperative detection algorithm under low SNR condition.For example, when the number of cooperative users is 9 and the transmit power is 10, the detection probability of the proposed algorithm in this paper can reach more than 90%, which is much higher than the detection performance of other spectrum detection algorithms. 

Cooperative Spectrum Sensing Based on LSTM-CNN Combination Network in Cognitive Radio System

Anti-jamming communication technology is one of the most critical technologies for establishing secure and reliable communication between unmanned aerial vehicles (UAVs) and ground units. The current research on anti-jamming technology focuses primarily on the power and spatial domains and does not target the issue of intelligent jammer attacks on communication channels. We propose a game-theoretical center frequency selection method for UAV-enabled air-to-ground (A2G) networks to address this challenge. Specifically, we model the central frequency selection problem as a Stackelberg game between the UAV and the jammer, where the UAV is the leader and the jammer is the follower. We develop a formal matrix structure for characterizing the payoff of the UAV and the jammer and theoretically prove that the mixed Nash equilibrium of such a bimatrix Stackelberg game is equivalent to the optimal solution of a linear programming model. Then, we propose an efficient game algorithm via linear programming. Building on this foundation, we champion an efficacious algorithm, underpinned by our novel linear programming solution paradigm, ensuring computational feasibility with polynomial time complexity. Simulation experiments show that our game-theoretical approach can achieve Nash equilibrium and outperform traditional schemes, including the Frequency-Hopping Spread Spectrum (FHSS) and the Random Selection (RS) schemes, in terms of higher payoff and better stability.

Adaptive Spectrum Anti-Jamming in UAV-Enabled Air-to-Ground Networks: A Bimatrix Stackelberg Game Approach

Underwater navigation is a challenging problem in the field of Autonomous Underwater Vehicles (AUVs) due to significant electromagnetic wave attenuation and uncertainties in the underwater environment. In general, the AUV's navigation system uses a number of different sensors, including Doppler Velocity Log (DVL) or transponders, etc., to perform the positioning task. Recently, the use of Visual Odometry (VO) based on underwater optical data or Acoustic Odometry (AO) based on high-frequency sonar data, to assist AUV navigation has become a potential research field. However, due to the different turbidity of seawater, insufficient light, and other factors, there are loopholes in the method based on a single sensor, which needs to be applied simultaneously to improve navigation quality and robustness. The use of multi-sensor navigation techniques can effectively achieve high navigation effects, but will also lead to an increase in the computational burden, which needs to be balanced. Therefore, this paper adopts a sensor fusion framework based on a parallel federated Unscented Kalman filter (UKF) to reduce the computation of the filter and employs the trapezoid formula to further improve the accuracy. The experimental data collected from the WEBOTS platform has been used to verify and compare different navigation methods. 

Acoustic-optic assisted multisensor navigation for autonomous underwater vehicles

Abstract To estimate the accurate position of the target, the geodesic curvature of the transmitter sensor is proposed in the present work. The geodesic curvature of the transmitter sensor traces the motion of a sensor as the shortest distance along a curved path, which measures the optimal distance on a curved path, accurate measurement of moving targets in an environment with complex geodesics, enhances the realism of the simulation, and underwater surveillance. Theorem 2.1 describes the geodesic curvature between the transmitter sensor and the target. A proposed non-cooperative moving target state measurement (NMTSM) algorithm quantifies the degradation of localization performance and mitigates the stratification effect and geodesic spreading losses. The localization error, localization error variance, and localization accuracy with respect to the number of anchor nodes are compared with standard methods. The proposed non-cooperative moving target state measurement (NMTSM) algorithm improves the localization performance by 33.46% and reduces position MSE by 35.46%, and velocity MSE by 38.86% up to 650 meters. It reduces the localization error by 32.23%, the localization error variance by 34.86% and improves the localization accuracy by 42.21%. The proposed algorithm is useful for the sub-aquatic Internet of Underwater Things (IoUT).

Position Computation of Underwater Target Using Geodesic Curvature for Sub-Aquatic Internet of Underwater Things

This paper presents a promising Deep-Learning (DL) approach for accurate symbol detection in a slow Frequency Hopping (SFH) wireless communication System under a Narrow Band (NB) multipath channel fading. A feedforward neural network with three layers of input, hidden, and output was employed for deep learning. The neural network is designed to take 80 features as input, representing the received signal samples at the receiver. The neural network is trained to anticipate the transmitted symbol based on the provided training dataset, utilizing different modulation techniques, including Binary Phase Shift Keying (BPSK), Quadrature Phase Shift Keying (QPSK), 8-PSK, and 16-PSK. Additionally, computer simulations are conducted to verify the effectiveness of the proposed method across various modulation schemes. The generated training loss and validation loss curves confirmed the ability of the receiver to learn.

Intelligent Receiver for Frequency Hopping Signals Using Deep Learning

The parameter estimation of multiple frequency-hopping (multiple FH) signals with frequency-switching time is a great challenge under conditions in which the number of signals is unknown. Due to the increasing mobility of devices such as unmanned aerial vehicles (UAVs), speed of parameter estimation is even more demanding. To solve this problem, an algorithm for estimating parameters of multiple FH signals based on compressed spectrum sensing and maximum likelihood (CSML) theory is proposed in this paper. First, the received signal is split into segments of the same length, and the frequencies contained in each segment are extracted using compressed spectrum sensing and kurtosis threshold. Next, the frequencies contained in adjacent segments are compared to find the signal segment in which frequency hopping occurs and its corresponding frequency. Finally, a hopping-time fast estimation algorithm based on the maximum likelihood theory is used to estimate the hopping time. Simulation results show that the algorithm proposed in this paper can estimate the parameters of multiple FH signals quickly and accurately when the number of signals is unknown and that it is equally effective for multiple FH signals with frequency-switching time.

/pdf/a-fast-estimation-algorithm-for-parameters-of-multiple-20b8zvz9.pdf

A Fast Estimation Algorithm for Parameters of Multiple Frequency-Hopping Signals Based on Compressed Spectrum Sensing and Maximum Likelihood

The prerequisite for underwater vehicles to accomplish various underwater tasks is to obtain real-time position information about themselves in the underwater environment using internal or external sensors, which is known as underwater positioning and navigation technology. This paper presents a general review of underwater vehicle positioning and navigation technology and illustrates the importance of underwater terrain-aided navigation technology. It also summarizes its general structure and describes the principles and functions of each component. The paper mainly analyzes various mainstream methods applied to underwater terrain-aided navigation, focuses on terrain suitability analysis and terrain matching algorithms, which play a central role in the analysis. Additionally, the research progress of terrain-aided navigation technology is described, and the research methods of terrain suitability analysis, terrain matching navigation, and the latest technology applications are compared. Some shortcomings of the current underwater terrain-aided navigation technology are presented, and future research directions are foreseen to provide a basis for subsequent research.

https://doi.org/10.1109/access.2023.3275078

Survey of Terrain-Aided Navigation Methods for Underwater Vehicles

Recently, contrast learning has become an important approach in unsupervised learning, which is very good at classifying and predicting unlabeled data, and the best contrast learning models in recent years have been able to achieve the prediction accuracy of supervised learning models. This paper analyzes the algorithmic ideas of contrast learning, discusses the typical contrast learning models in recent years, analyzes their model structures, compares the features and differences among the models, and looks forward to the future directions of contrast learning methods that need to be improved, providing valuable references for subsequent engineering application research.

A Survey of Comparative Learning Methods

Offshore channel clearance is an essential underwater task to protect vessels and divers effectively, but current underwater target classification relies heavily on operator identification. Machine learning provides highly accurate methods for image classification as well as detection. In this paper, a new hybrid quantum-classical classification algorithm is proposed. It uses quantum devices to reduce dimension and denoise data sets, greatly reducing the difficulty of classical computer processing data. Using abundant classical classification algorithms, the classification problem of different scenarios can be processed, improving the classification efficiency. Using two kinds of underwater object data sets as examples, the numerical simulation results show that the quantum algorithm can accurately achieve dimensionality reduction. This hybrid algorithm has polynomial acceleration in dimension reduction than classical methods, even considering the classical readout of quantum data. The results also show that the classification accuracy of the training set improves from 0.772 to 0.821 compared to the original dataset. Furthermore, different classical classifiers can be selected in the case of different objects, so this hybrid algorithm has broad application prospects in different fields.

/pdf/a-hybrid-quantum-classical-algorithm-for-underwater-target-2e7yqwhe.pdf

A Hybrid Quantum-Classical Algorithm for Underwater Target Classification

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A Fast Estimation Algorithm for Parameters of Multiple Frequency-Hopping Signals Based on Compressed Spectrum Sensing and Maximum Likelihood

Survey of Terrain-Aided Navigation Methods for Underwater Vehicles

A Survey of Comparative Learning Methods

A Hybrid Quantum-Classical Algorithm for Underwater Target Classification