Adversarial patch-based physical attacks have attracted growing attention, yet their effectiveness in deceiving aerial detection systems remains underexplored. Existing methods typically rely on carefully designed adversarial patches with specific shapes, strategically placed in or around the target. However, for small-scale remote sensing objects, these approaches often suffer from limited attack efficacy and poor transferability. Furthermore, the conspicuous nature of these patches makes them easily identifiable by human observers, undermining the fundamental principle of adversarial examples. To address these challenges, this article proposes a novel camouflage coating attack (CCA), a physical attack method specifically designed for remote sensing targets, exhibiting high attack effectiveness and strong transferability. During training, the adversarial coating is precisely applied to the target using a dedicated coating application algorithm, followed by pixel-level iterative optimization to enhance its real-world attack efficacy. To improve stealthiness, we introduce a sophisticated loss function that generates covert adversarial patterns while preserving camouflage characteristics. We rigorously evaluate CCA through black-box and white-box attack experiments on multiple object detectors, along with transferability assessments and perceptibility analyses of the generated camouflage patterns. Experimental results demonstrate that CCA achieves an attack success rate (ASR) of up to 82.96%, reducing the average precision (AP) to as low as 14.12%. This study establishes a new benchmark for assessing the adversarial robustness of object detectors and their defense mechanisms. By introducing this novel attack paradigm, we aim to inspire future advancements and challenges in adversarial research in the domains of remote sensing and object detection.

Aiming for efficient coordination mechanisms for the distributed weighted set cover problem, we study from ordinal potential game theoretic learning and propose a Nash equilibrium selection algorithm (NESA). An ordinal potential game model is established, where the local utility function is designed by incorporating a greedy heuristic. To distinguish Nash equilibria of different global fitness, we further classify them into the inferior Nash equilibrium (INE) and the superior Nash equilibrium (SNE), and show that the optimal solution must be an SNE. High-quality SNE solutions are obtained by assigning each player a local stochastic rule based on its category and a finite memory. By demonstrating the existence of a finite improvement path from each INE to an SNE, we prove finite-time convergence of the NESA. Numerical experiments are carried out and comparisons against representative methods are presented, which demonstrate the effectiveness as well as the superiority of our methodology to the state-of-the-art.

Translational motion compensation plays an important role in the inverse synthetic aperture radar (ISAR) imagery. In this study, a new translational motion compensation algorithm for ISAR imaging under low signal-to-noise ratio (SNR) conditions is proposed. This method is formed based on the optimization of dynamic image sharpness metric, by which the translational parameters are accurately estimated from the returned signals. The important properties of the locally and globally optimal points of dynamic image sharpness function are proved and discussed by first using the dominant point-targets model. These properties are employed in the scheme to search for the globally optimal point and prevent the optimization being trapped at a locally optimal point. Based on the properties of optimal points and Gauss–Newton method, the algorithm to estimate the translational parameters by dynamic image sharpness metric optimization (DISMO) is devised. The DISMO can find the accurate translational parameters corresponding to the globally optimal point without being affected by local optima under low SNR conditions with high efficiency. Further, the translational compensation is completed based on the estimates. The proposed method is applied to simulated and real data. The processing results confirm the effectiveness of this new algorithm.

Toward better approximation for the minimum-weighted vertex cover (MWVC) problem in multiagent systems, we present a distributed algorithm from the perspective of learning in games. For self-organized coordination and optimization, we see each vertex as a potential game player who makes decisions using local information of its own and the immediate neighbors. The resulting Nash equilibrium is classified into two categories, i.e., the inferior Nash equilibrium (INE) and the dominant Nash equilibrium (DNE). We show that the optimal solution must be a DNE. To achieve better approximation ratios, local rules of perturbation and weighted memory are designed, with the former destroying the stability of an INE and the latter facilitating the refinement of a DNE. By showing the existence of an improvement path from any INE to a DNE, we prove that when the memory length is larger than 1, our algorithm converges in finite time to DNEs, which could not be improved by exchanging the action of a selected node with all its unselected neighbors. Moreover, additional freedom for solution efficiency refinement is provided by increasing the memory length. Finally, intensive comparison experiments demonstrate the superiority of the presented methodology to the state of the art, both in solution efficiency and computation speed.

Area coverage and network lifetime are two contradictory issues to the architecture development of a wireless sensor network (WSN). A satisfactory balance could be achieved by deploying abundant sensor nodes randomly and dividing them into$k$exclusive cover sets. Toward self-organized partition with higher efficiency, we address the problem from the perspective of networked potential games and propose a memorial mixed-response algorithm (MMRA), which is implemented in a distributed and synchronous manner. Being viewed as a game player, each sensor node first updates its memory using a temporary action, which is generated by following a mixed response rule. After this, the coordination evolves into the next iteration by each player randomly drawing an action from its memory with equal probabilities. We prove that our algorithm converges with probability 1 to a convention of Nash equilibria, with the worst approximation ratio strictly larger than 0.5. Moreover, it is also found that a tradeoff between solution efficiency and computation time could be achieved via the adjustment of the amount of randomness introduced via the memory length$m$as well as the probability$p_{m}$, where better partition results are more likely to be generated using a larger$m$and smaller$p_{m}$. Comparisons with existing distributed methods demonstrate the superiority of our method in terms of solution refinement as well as convergence speed.

Toward the minimal weighted vertex cover (MWVC) in agent-based networking systems, this paper recasts it as a potential game and proposes a distributed learning algorithm based on relaxed greed and finite memory. With the concept of convention, we prove that our algorithm converges with probability 1 to Nash equilibria, which serve as the bridge connecting the game and the MWVC. More importantly, an additional degree of freedom is also provided for equilibrium refinement, such that increasing memory lengths and mutation probabilities contributes to the improvement of system-level objectives. Comparisons with typical methods, centralized and distributed, demonstrate the advantage of our algorithm for both weighted and unweighted versions. This paper not only provides a useful tool for the MWVC problem in decentralized environments but also paves an effective way for distributed coordination and optimization that could be modeled as potential games.

The new method stated in this paper is to model the multiple objects in the visual sequence into two-dimensional multi-peak probability distribution, which raised a new multiple-objects tracking method with particle filter. The results of importance resampling by the particle filter represent the probability distributions of the objects. Firstly, it gains the probability distribution model points of each object through mean-shift algorithm, and FCM (Fuzzy C - Means) is used to get the particle subset of the respective objects. Then final state of each object can be estimated and mean-shift kernel bandwidth parameter can be updated through particle subset. Finally, the movement of the objects can be tracked through data association. Experiments prove that this algorithm can be more effectively and more stably applied onto the tracking of multiple-objects complicated movements, such as spinning, zooming, masking, etc.