In order to overcome the vulnerability of the global navigation satellite system (GNSS) and provide robust position, navigation and time (PNT) information in marine navigation, the autonomous positioning system based on ranging-mode Automatic Identification System (AIS) is presented in the paper. The principle of the AIS autonomous positioning system (AAPS) is investigated, including the position algorithm, the signal measurement technique, the geometric dilution of precision, the time synchronization technique and the additional secondary factor correction technique. In order to validate the proposed AAPS, a verification system has been established in the Xinghai sea region of Dalian (China). Static and dynamic positioning experiments are performed. The original function of the AIS in the AAPS is not influenced. The experimental results show that the positioning precision of the AAPS is better than 10 m in the area with good geometric dilution of precision (GDOP) by the additional secondary factor correction technology. This is the most economical solution for a land-based positioning system to complement the GNSS for the navigation safety of vessels sailing along coasts.

In the task of Synthetic Aperture Radar (SAR) ship detection, ship targets exhibit arbitrary orientations and are closely aligned. In recent years, oriented bounding box (OBB) based detectors have gained attention as a solution to the significant overlap issue present in horizontal bounding box (HBB) based detectors. Due to the boundary discontinuity issue with current mainstream OBB methods, detectors utilizing the polar coordinate system to define OBB have been introduced. However, currently available methods are usually complicated to encode and decode, and do not take into account the complex background of SAR images. This results in the complexity of neural networks and inaccurate predictions. In this paper, we propose a novel five-parameter polar coordinate dense regression detector (FPDDet) that only uses a centroid, a mapped polar diameter, and two polar angles to deal with the overlap problem of HBB and the boundary discontinuity problem of OBB. Meanwhile, we introduce a dense regression strategy based on covariance-adaptive rotated Gaussian heatmap to dynamically assign ship target samples in response to large-scale variation of ship targets in SAR images, and we suggest a dense regression heatmap loss function to better match our dense regression strategy. In addition, we design a feature enhancement module to enhance the target features while weakening the background interference, aiming to cope with the severe noise pollution of SAR images. Experimental results show that our FPDDet achieves the state-of-the-art performance on Rotating SAR Ship Detection Dataset (RSSDD) and Rotated Ship Detection Dataset (RSDD). Compared to previous best results, mAP has been improved by 1.2% and 1.71%, respectively.

With the development of new Internet of Things (IoT) applications, such as unmanned ground vehicle (UGV), unmanned aerial vehicle (UAV), and unmanned surface vessel (USV), the security of wireless device identification is getting more and more attention. The radio frequency fingerprint (RFF) technology utilizes the unique hardware characteristics of the device for identity authentication, which overcomes the security risks of traditional authentication schemes based on distributable keys. In this paper, we propose an RFF identification scheme based on logarithmic power cosine spectrum for transient signals. We use the sliding window variance trajectory-based posterior probability density detection algorithm to improve the efficiency of transient signal detection, then adopt the discrete Fourier transform to extract the logarithmic envelope power spectrum of the transient signal, after that utilize the cosine transform to compresses the frequency domain features, and finally construct the 7-dimensional RFF feature vector. For the application scenario of USV, we acquire the radio frequency signals of 7 marine automatic identification systems (AIS) as the test dataset. The experimental results show that the proposed scheme can achieve ideal recognition accuracy using a Support Vector Machine as the classifier. Meanwhile, we use Principal Component Analysis to verify that the 7-dimensional RFF feature vector has good distinguishability for different wireless devices.

In order to use the existing automatic identification system (AIS) to provide additional navigation and positioning services, a complete pseudorange measurements solution is presented in this paper. Through the mathematical analysis of the AIS signal, the bit-0-phases in the digital sequences were determined as the timestamps. Monte Carlo simulation was carried out to compare the accuracy of the zero-crossing and differential peak, which are two timestamp detection methods in the additive white Gaussian noise (AWGN) channel. Considering the low-speed and low-dynamic motion characteristics of ships, an optimal estimation method based on the minimum mean square error is proposed to improve detection accuracy. Furthermore, the α difference filter algorithm was used to achieve the fusion of the optimal estimation results of the two detection methods. The results show that the algorithm can greatly improve the accuracy of pseudorange estimation under low signal-to-noise ratio (SNR) conditions. In order to verify the effectiveness of the scheme, prototypes containing the measurement scheme were developed and field tests in Xinghai Bay of Dalian (China) were performed. The test results show that the pseudorange measurement accuracy was better than 28 m (σ) without any modification of the existing AIS system.

In order to provide resilient position, navigation, and time (PNT) information forE-Navigation, the ranging-mode (R-Mode) positioning using automatic identification system (AIS) signals is encouraged. As the accuracy is the key for the positioning system, this paper investigates the position error of the R-Mode positioning based on AIS shore-based station in China. The measurement errors of Gaussian filtered minimum shift keying (GMSK) demodulation based on carrier phase locking loop are investigated in theory. The dilution of precision (DOP) for time of arrival (TOA) and time difference of arrival (TDOA) used in R-Mode positioning of AIS is discussed in two measurement mechanisms. The positioning error distributions in the North, East, and South Sea regions of China based on the existing AIS shore-based stations are evaluated. The positioning accuracy is at the meter level in the most traffic dense areas to meet the requirements for vessel navigation.

Collaborative spectral sensing can fuse the perceived results of multiple cognitive users, and thus will improve the accuracy of perceived results. However, the multi-source features of the perceived results result in security problems in the system. When there is a high probability of a malicious user attack, the traditional algorithm can correctly identify the malicious users. However, when the probability of attack by malicious users is reduced, it is almost impossible to use the traditional algorithm to correctly distinguish between honest users and malicious users, which greatly reduces the perceived performance. To address the problem above, based on the β function and the feedback iteration mathematical method, this paper proposes a malicious user identification algorithm under multi-channel cooperative conditions (β-MIAMC), which involves comprehensively assessing the cognitive user’s performance on multiple sub-channels to identify the malicious user. Simulation results show under the same attack probability, compared with the traditional algorithm, the β-MIAMC algorithm can more accurately identify the malicious users, reducing the false alarm probability of malicious users by more than 20%. When the attack probability is greater than 7%, the proposed algorithm can identify the malicious users with 100% certainty.