Reinforcement learning (RL) has been used for uncrewed aerial vehicle (UAV)-based jamming detection by selecting hypothesis-test thresholds on received signal strength (RSS) to safeguard large language model (LLM) inference over wireless links. However, existing methods struggle against smart jammers with dynamic jamming probability. In this paper, we propose a UAV-based jamming detection scheme for LLM-enabled wireless networks to detect smart jamming. The proposed scheme augments traditional wireless features with an LLM-inferred communication environment indicator derived from multimodal sensing (e.g., images/videos from mobile devices), improving accuracy beyond RSS alone. Our approach jointly optimizes the test threshold and the UAV’s sniffer channel assignment, even with a limited number of antennas. We achieve this via a RL policy that leverages UAV imagery, e.g., suspicious radio devices and measuring relative distances, to accelerate detection speed. We implement the proposed scheme on a UAV equipped with a universal software radio peripheral to alert on jamming attacks in a wireless network integrated with a 7-billion-parameter vision-language multimodal LLM. Experiments with a UAV at 3 m detecting a smart jammer during image–text LLM inference across three mobile devices and an edge server demonstrate improvements in both detection accuracy and speed.

Reinforcement learning based jamming detection that chooses the test threshold to evaluate the received signal strength indicator (RSSI) and the packet loss rate is inaccurate for unmanned aerial vehicles (UAVs) lacking target indication against smart jammers. In this paper, we propose a UAV swarm-enabled multimodal jamming detection scheme to optimize the test thresholds based on vision information such as object classes and relative distances, along with the RSSI, the channel gain and the communication performance, including packet delivery ratio, bit error rate and packet delivery delay. A machine learning classifier is used to assess the joint variation among RSSI, channel gain and communication performance, with the output outlier score compared with the test threshold to detect the jammer. The detection results shared from neighboring UAVs are exploited in the update of policy distribution to refine jamming signal resolution and thus reduce the miss detection rate. The utility bound is derived based on the Nash equilibrium of the jamming detection game between the UAV swarm and the jammer. Experimental results based on 5 UAVs to detect a smart jammer show that our proposed scheme enhances the detection accuracy compared with the benchmarks.

In order to reduce the probability of leakage and improve detection accuracy, multiple radar equipment is required to perform multiple detections on the same airspace. Due to the overlap of airspace, there are multiple local tracks reported by various radar equipment that belong to the same target. The local tracks reported by each radar require track fusion to form a system track, and track association is a prerequisite for track fusion. This paper proposes a multi-target and multi-sensor track association analysis method, which can distinguish whether local tracks come from the same target and associate local tracks with the same target, providing conditions for track fusion to form system tracks. This method divides track association into two steps: Coarse association and fine association, which greatly improves the accuracy of track association.

Ballistic missile attack-defense confrontation is a typical system-of-systems confrontation process. Ballistic missile early warning operations have the characteristics of high time-sensitivity, high complexity and high dynamic. It is impossible to deal with complex battlefield situations at the moment of decision. It is necessary to develop a detection plan in advance for possible battlefield situations, and intelligently recommend the optimal detection plan for the commander to make decisions based on the battlefield situation. The ballistic missile early warning detection plan is designed from three aspects: the design of search screen parameters, the determination of search data rate, and the allocation strategy of search resources in different airspace; Establish the evaluation indicator system and evaluation model of the ballistic missile early warning detection plan, evaluate and optimize multiple detection plans, and automatically recommend them to the commander according to the advantages and disadvantages of the operational effectiveness, connect the auxiliary decision-making chain of “design evaluation recommendation decision”, providing support for the intelligent decision-making of the ballistic missile early warning operation.

In this study, we extend traditional (single-target) hybrid systems to multi-target hybrid systems with a focus on the multi-maneuvering-target tracking system. This system consists of a continuous state, a discrete and switchable state, and a discrete, time-constant, and unique state. By defining a new generalized labeled multi-Bernoulli density, we prove that it is closed under the Chapman-Kolmogorov prediction and Bayes update for multi-target hybrid systems. In other words, we provide the exact derivation of a solution to this system, i.e., the multi-model generalized labeled multi-Bernoulli filter, which has been developed without strict proof.

A great deal of interest has been paid to computation problem of Dezert-Smarandache theory (DSmT). But there are still problems on complex analysis and frequently search. The computational measurement of DSmT is presented in which the computation is generated in the search for focal elements, the combination of focal elements and basic belief assignment, the expression of focal elements. A new DSmT computational optimization approach is presented to solve the problems. The proposed approach optimizes the original evidence and combination of focal elements. The original evidence is reduced to keep the effective focal elements. And the focal element relationship is integrated into evidence code to realize self-adaption for combination of focal elements. Numerical results are provided to validate our approach.

This paper proposes an algorithm of building keypoint matches on multimodal images by combining a bootstrap process and global information. The correct ratio of keypoint matches built with descriptors is typically very low on multimodal images of large spectral difference. To identify correct matches, global information is utilized for evaluating keypoint matches and a bootstrap technique is employed to reduce the computational cost. A keypoint match determines a transformation T and a similarity metric between the reference and the transformed test image by T. The similarity metric encodes global information over entire images, and hence, a higher similarity indicates the match can bring more image content into alignment, implying it tends to be correct. Unfortunately, exhausting triplets/quadruples of matches for affine/projective transformation is computationally intractable, when the number of keypoints is large. To reduce the computational cost, a bootstrap technique is employed that starts from single matches for a translation and rotation model, and goes increasingly to quadruples of four matches for a projective model. The global information screens for "good" matches at each stage and the bootstrap strategy makes the screening process computationally feasible. Experimental results show that the proposed method can establish reliable keypoint matches on challenging multimodal images of strong multimodality.

This letter proposes a multiresolution technique to address the high computational cost in remote sensing image registration. The scale-invariant feature transform is applied to detect keypoints and descriptors, and then, global information combined with descriptors is utilized to establish keypoint mappings. Keypoints are first classified according to their octaves. Then, in the lowest resolution, the keypoints of the largest octave are mapped with descriptors and the global information, giving an initial affine transformation$T_0$. In the next octave, the keypoints of the second largest octave are mapped by employing$T_0$to narrow the space of matching keypoints. By this means, the process of establishing keypoint correspondences is conducted from one resolution (octave) to the next as the obtained transformation gets finer until we get to the highest resolution. Due to the high computational expense of computing global information, the proposed technique is important for aligning large-size remote sensing imagery. Experimental results show that the proposed method can achieve a comparable registration accuracy but with a less computational cost than directly building keypoint mappings on images of large size.