Timely detection and localization of non-cooperative unmanned aerial vehicles (UAVs) in complex urban environments is essential for low-altitude security monitoring and rapid response. However, single-modality sensing often suffers from occlusion, multipath, non-line-of-sight propagation, and constrained backhaul capacity, thereby degrading long-range detectability and localization accuracy. These limitations motivate multimodal sensing to improve robustness via complementary measurements. This paper presents a distributed ground-based multimodal sensing network and an online Mutual-Information-Driven Adaptive Scheduling (MIDAS) algorithm. The proposed system integrates received-signal-strength (RSS)-based RF sensing with acoustic angle-of-arrival (AoA) sensing, while an optical module can be triggered for high-precision refinement and tracking. Under bandwidth constraints, MIDAS employs an information-theoretic greedy policy that selects a subset of sensors for cooperative localization by maximizing the incremental mutual information. At the fusion center, covariance-weighted fusion is performed to suppress low-reliability modalities. To address RF-silent UAVs, we incorporate a degradation-aware acoustic fallback selection strategy to maintain localization during RF outages. System-level simulations over multiple urban ingress routes and UAV types demonstrate robust performance under heterogeneous sensing conditions.

ABSTRACT Spectrum maps are visualization tools that reflect the underlying spectral environment, enabling advanced functions such as spectrum decision‐making and emitter identification. To enhance mapping accuracy and optimize resource utilization, this study addresses the sensor node selection problem in ground‐based sensing scenarios. We propose an entropy‐greedy node selection (EGNS) framework that employs a two‐stage scheduling strategy: the first stage performs coarse sensing via spatial sector partitioning to obtain an initial estimate of emitter locations, and the second stage executes an enhanced greedy selection algorithm to iteratively minimize the signal reconstruction error. Simulation results on real‐world spectrum datasets show that the proposed method achieves superior reconstruction accuracy and lower sensing costs compared to conventional sampling approaches, making it well‐suited for dynamic electromagnetic monitoring applications under constrained budgets.

Regulation of drones is already an important research topic. The drone identification method based on the radio frequency (RF) signal analysis technology is an efficient approach to regulate mainstream civilian drones. However, existing research on drone identification has been conducted under high signal-to-noise ratio (SNR) conditions. In fact, long-range drone identification in urban environments is performed under low SNR conditions. In this article, a global context (GC)-based threshold strategy is proposed to solve the above problem. The threshold is automatically determined during the deep architecture optimization. Meanwhile, the unimportant features in the network are removed by threshold denoising. First, the weight of each feature channel is obtained by modeling cross-channel dependencies, and then a weighted average is performed for each position along the feature channel on the feature map to obtain the thresholds. The method reduces the impact of unimportant features on performance without complex data preprocessing. In the experiments, the performance and complexity of the proposed method are extensively evaluated by 11 different drone RF signals. The experimental results show that in comparison with other deep learning-based threshold calculation methods, the proposed method has a more outstanding performance and lower computational complexity for drone RF signal identification under the low SNR condition.

This paper investigates the anti-jamming problems for multi-user scenarios. On the one hand, users in the networks should coordinate their channel selection strategies to avoid spectrum conflicts among different users. On the other hand, because of the openness characteristic of wireless communications, malicious jammers can disrupt the legitimate communications of legitimate users by sending jamming signals, thus users also need to fully consider how to defend against malicious jamming attacks. To cope with the internal coordination and external confrontation problem, a context-aware dynamic spectrum coordinated anti-jamming approach is proposed. In detail, the multiuser anti-jamming scenario is modeled as an anti-jamming local altruistic game, and the existence of Nash Equilibrium (NE) is demonstrated. Besides, the proposed game model is proved to be an exact potential game. To obtain NEs, a multi-pattern stochastic learning algorithm (MSLA) is designed. Through local information exchange and distributed learning, users can achieve global optimization under the dynamic jamming environment.

This paper investigates the anti-jamming problems in wireless communication networks. In these networks consisted of multiple devices (users), there exist two critical problems. On the one hand, users with various transmission requirements should coordinate their channel selection strategies distributedly to avoid spectrum conflicts and satisfy transmission demands. On the other hand, they also need to fully consider how to eliminate the effects of malicious attacks. To cope with the internal coordination and external confrontation challenges and accommodate the dynamic changing jamming attacks, a context-aware distributed coordinated anti-jamming channel access mechanism is proposed, which means for different cases of jamming attacks, different access strategies are adopted. In detail, to reflect the heterogeneous communication demands of users, the transmission satisfaction function is firstly introduced. Then, the multi-user anti-jamming scenario is modeled as a context-aware multi-pattern dynamic anti-jamming game, which can be decomposed into two sub-games. Here, for the case that the control channel is available, a local altruistic sub-game is introduced. While for the case that the control channel has been jammed, an anti-jamming congestion sub-game is designed. Besides, the existence of Nash Equilibriums is demonstrated. To obtain NEs, a context-aware distributed channel access (CDCA) algorithm is designed. Through game-theoretic analysis and distributed learning, global transmission satisfaction can be improved under the dynamic jamming environment. Furthermore, the fairness of the network can also be guaranteed.

In order to promote the spectral utilization, this article investigates the partially overlapping channel (POC) accessing problem in the wireless network. To reflect the heterogeneous characteristics of users, the optimization goal is set as maximizing the quality of service (QoE), instead of maximizing the throughput or minimizing the interference. The problem is formulated as a QoE maximization game and is then proved to be an ordinal potential game by utilizing the approximate relationship between interference and QoE. The proposed game is proved to have at least one pure Nash equilibrium (NE) and the best pure strategy NE point is an approximate global optimum of maximizing network QoE. A distributed algorithm is designed to reach the NE and it is proved that when the learning parameter is large enough, the algorithm asymptotically maximizes the network QoE. Simulation results verify the effectiveness of utilizing POCs and the proposed method.