Synthetic aperture radar (SAR) altimeter is able to measure height with high precision, which has been extensively applied to airborne aircrafts for positioning. With the assumption of a flat surface following a Gaussian distribution, the height is obtained by retracking the waveform. However, this assumption often fails in a complex terrain, leading to unpredictable height bias. In this article, a slope-based height correction (SHC) toward robust height inversion in complex terrain is proposed using linear terrain decomposition. Based on the radar propagation equation, the impact of the topography on height inversion is investigated. Then, the response from complex topography can be divided into a determined part related to a set of primary slopes and a stochastic part with certain undulation. Additionally, the height bias induced by the slopes is given based on power constraints. The error bound of the height correction is also derived correspondingly. The main advantage of the proposed method is reliable height correction with the prior digital elevation model (DEM). Experimental results based on both simulated and real data demonstrate a significant decrease in height bias, which greatly extends the application of the altimeter.

The Digital Twin (DT) represents a rapidly advancing technological innovation within the Industrial Internet of Things (IIoT) domain. DT leverages the power of simulation, machine learning, and data mining to facilitate optimal decision-making for physical objects. However, the creation of a dynamic and living digital counterpart comes at a considerable cost. It requires continuous massive data updating and processing every time the physical object changes. As most data collected by IIoT devices are in their original form, such as images and videos, transmitting such data to remote cloud computing will result in large delays. Furthermore, data processing is often a computationally intensive operation, such as image recognition and video coding, making it impractical to perform processing tasks directly in IIoT devices. To overcome this problem, we introduced the Multi-access/mobile Edge Computing (MEC) architecture to enhance capabilities of DT-enabled IIoT devices. IIoT devices can leverage the extra computing resources in MEC to process raw data, transmitting only the calculation results to update the digital counterpart. To efficiently allocate resources between IIoT devices and MEC, we propose a double auction-based resource allocation scheme. The IIoT devices can purchase computing power from MEC, and an iterative double auction scheme is applied to achieve system efficiency within this market. Furthermore, we propose the Win or Learn Fast Algorithm Policy Hill Climbing (Wolf-PHC) algorithm, which enables agents to improve their strategies continuously through participation in auctions. Simulation results demonstrate that this algorithm accelerates the process of market equilibrium convergence.

Synthetic aperture radar (SAR) altimetry has the capability of altimetry with a good accuracy, especially for the ocean scene. The model-based methods are widely used in the height inversion of the altimetry data due to the strong correlation with physical process. However, the bias of the model will significantly decrease performance of height inversion. In this work, we introduce a novel height inversion algorithm based on the closed-form expression, which is an effective model for measuring radar echoes. Effective initialization strategy expands the application scope, and two-step retracking algorithm is designed which could be used in an iterative least-squares algorithm. In essence, this method aims to remove the coupling between the parameters in closed-form expression. Experimental results using the simulated data indicate that this proposed method has wider application range under different signal-to-noise condition. In addition, precision of two-step method remains stable compared with traditional method, which has potential for actual altimetry system.

The combination of digital twin (DT) and Internet of Vehicles (IoV) has gained significant attention from both academia and industry in recent times. DT can establish a high fidelity virtual representation of IoV based on the real-time sensor data, and feedback the decision policy, therefore generating possible improvements. Especially, as the advance of Mobile Edge Computing (MEC) technique, it has the potential to facilitate digital twin¡¯s computationally intensive tasks. However, how to schedule the MEC computing resource is the key to efficient operation of the whole system. Therefore, this study aims to investigate pricing considerations and resource management that exist between the vehicle and MEC server in order to mitigate this issue. Specifically, we model the interaction between the MEC server and vehicles as a Stackelberg game, where the leader (i.e., the MEC service provider) sets prices, and then the vehicles act as followers. By leveraging information about social interactions from other vehicles, utility functions are formulated by the vehicles. Additionally, the study analyzes the existence and uniqueness of the Stackelberg equilibrium, and proposes a dynamic iterative algorithm to find the appropriate Nash equilibrium for the proposed Stackelberg game. Experimental results demonstrate that the proposed scheme effectively formulates suitable prices and meets computational requirements.

Convolutional neural networks (CNNs) have been widely used in SAR (Synthetic Aperture Radar) target recognition, which can extract feature automatically. However, due to its own structural flaws, CNNs are easy to be fooled by adversarial examples, even if they have excellent performance. In this letter, a novel attack named scattering center model attack (SCMA) is designed, and its generation process does not rely on the prior knowledge of any neural network. Therefore, we can get a stable way which can be applied to any neural network. In addition, an improved scattering center model extraction method, which is the pre-part of SCMA, can filter out the useless noise to optimize the stability of attack. In the experiment, SCMA is compared with advanced attack algorithms. From the experimental results, it is clear to find that SCMA has excellent performance in terms of transfer attack success rate. Furthermore, visualization and interpretability analysis underpin the theoretical feasibility of SCMA.