The rapid proliferation of unmanned aerial vehicles (UAVs) has increasingly posed significant challenges for airspace security, particularly under long-range and visually degraded conditions. Effective UAV recognition is thus critical, yet current methodologies typically depend on single-sensor inputs, such as infrared (IR) imaging and radio frequency (RF) analysis, which suffer inherent limitations in complex environments. Although multimodal sensing has been explored in UAV detection, the joint exploitation of IR imagery and RF signals for UAV type recognition remains largely underexplored. The structural heterogeneity between IR and RF features presents challenges for joint representation and decision-making, which remains underexplored in previous work. To address this gap, this paper proposes RF-IRSynNet, a multimodal UAV classification framework that integrates IR imagery and in-flight RF emissions to enhance recognition performance. In RF-IRSynNet, IR images are processed using YOLOv11 to detect UAV candidates and extract structured semantic features. Meanwhile, RF signals are modeled using reservoir computing, which efficiently encodes temporal and spectral dynamics via feature sequences. These modalities are fused through an adaptive confidence-weighted soft-voting strategy, dynamically balancing their contributions based on specific tasks. Experimental results demonstrate that RF-IRSynNet outperforms both unimodal baselines and existing multimodal approaches, achieving robust classification at long ranges. The framework maintains high accuracy even with reduced training data, indicating high efficiency for real-world UAV monitoring.

Azimuth ambiguity significantly degrades the quality of synthetic aperture radar images. Sub-look spectral analysis (SSA) is a common ambiguity-detection method, but its performance is limited by threshold sensitivity and the high correlation of specific ambiguities across sub-looks. To overcome these specific limitations, this paper proposes an improved detection method. It first increases the number of sub-looks and constructs a high-dimensional multi-look matrix to enrich the coherence differences between targets and ambiguities. Non-negative matrix factorization is then employed to decompose this matrix, effectively separating the coherent target components from the variably coherent ambiguity components without relying on predefined thresholds. Experimental results on real data demonstrate that the proposed improvements achieve superior azimuth-ambiguity-detection performance compared with conventional SSA methods.

Accurately sensing the channel state of heterogeneous networks is key to matching users’ diverse service communication demands with the channel state, and is an effective way to improve the utilization efficiency of network resource. However, existing channel state perception methods are not suitable for heterogeneous network, and their perception performance is easily affected by interference uncertainty. In order to achieve channel state perception of heterogeneous networks, this paper adopts a centralized collaborative perception model, where each node obtains local channel state perception results based on statistical pulse parameters at the physical layer. In order to reduce the impact of interference on perception performance, this paper uses the Jousselme distance to quantify the degree of difference among nodes caused by interference. Using the average credibility as a threshold, nodes in the sensing area are classified. On this basis, the local perception results of each node are performed classification-based correction to improve the accuracy and reliability of channel state perception. Simulation results indicate that the proposed method has good adaptability for channel state perception in complex electromagnetic environments. The perception results can accurately reflect the actual channel state, which is conducive to improving the network throughput.

To enhance the real-time performance and accuracy of guidance command generation, we propose an online reentry guidance algorithm based on analytical solutions of the hypersonic glide trajectory (HGT). Initially, an altitude-velocity profile is designed in the longitudinal plane to satisfy both path and terminal constraints. Based on this profile, we derive analytical solutions for the flight path angle (FPA) and bank angle. Subsequently, by employing the Newton-Raphson method to linearize the reentry motion equations, analytical solutions for the latitude and heading angle are obtained. Furthermore, we introduce an improved particle swarm optimization (IPSO) algorithm to optimize the profile parameters. This approach significantly enhances the algorithm’s global convergence by narrowing the parameter optimization range and adaptively adjusting the inertia weight and cognitive factors. Finally, we present an online guidance algorithm that combines the HGT analytical solutions with the IPSO algorithm. This algorithm effectively achieves longitudinal and lateral guidance by continuously updating the altitude-velocity profile and bank angle symbol in real time. Simulation results demonstrate that the proposed algorithm is fast, efficient, accurate, and holds significant potential for broader application.

A millimeter-wave (mm-Wave) dual circularly polarized (CP) antenna in gap waveguide (GWG) technology with high port isolation is proposed in this paper. It is consisted of a simplified orthomode transducer (OMT) and an improved multi-section hexagonal waveguide CP horn antenna. The OMT is composed of two metal layers without the traditional septum or iris, which makes the structure simpler. The CP horn antenna can be easily integrated with the OMT without mode conversion. The principle analysis as well as the simulated and measured results of the proposed antenna are given in this paper. The simulated and measured results agree very well with each other. The port isolation of more than 27 dB over bandwidth of 26.5−31 GHz (|S₁₁|< −15 dB) is achieved with relative bandwidth of 15.7%. The axial ratio (AR) lower than 2.5 dB for both left-hand and right-hand CP (LHCP and RHCP) are achieved over the bandwidth. The proposed antenna is a candidate for mm-Wave satellite communications or beyond fifth-generation (5G) communications applications.

Extracting infrared small targets from heterogeneous backgrounds remains a challenging task, as these targets lack salient texture and morphological features while the backgrounds are cluttered with noise. Therefore, effectively extracting discriminative features is essential for achieving complete and accurate detection. To address this issue, this paper proposes an algorithmic framework based on robust principal component analysis (RPCA), specifically designed for infrared small target detection in complex backgrounds. First, infrared small target detection is formulated as a generalized RPCA problem. A discriminative and reconstructive dictionary is constructed using supervised learning. Next, by introducing an ideal regularization term, the infrared image is reconstructed without losing structural information, yielding a discriminative principal component representation with respect to the learned dictionary. Finally, the structural features of infrared small targets are reconstructed via sparse coding, thereby enabling the extraction of infrared small targets. Extensive experimental results demonstrate the effectiveness of the proposed method.

Integrating prognostics and health management (PHM) with the existing maintenance support system of systems plays an important role in implementing reliability-centered maintenance (RCM). However, the increasing complexity and integration of civil aircraft systems pose challenges for conventional document-based systems engineering (DBSE) practice. Aiming at the specific problems of poor modeling degree, weak traceability between problem and solution domains, and insufficient integration in civil aircraft PHM development, a model-based systems engineering (MBSE) approach is adopted to overcome the limitations of DBSE method. This paper proposes a structured integrated modeling method to facilitate PHM functional integration with other aircraft systems. An MBSE modeling method based on traceable requirements, functional, logical, and physical models, is applied in the integration process. Additionally, a multi-viewpoint analysis method within the Department of Defense Architecture Framework (DoDAF) is introduced to illustrate the modeling elements and processes from a multi-dimensional perspective. The modeling logic and architecture are subsequently presented, followed by examples of requirements, functional flows, and resource flows models using the systems modeling language (SysML). Finally, a preliminary logic simulation verification is conducted as a case study of typical PHM functional integration with maintenance support. The case study results demonstrate that the proposed method enhances information traceability and consistency, which can offer theoretical support and technical reference for the development of maintenance support system.

The evaluation of the equipment contribution rate to system-of-systems (CRSoS) is crucial for optimizing the armament system-of-systems structure and enhancing combat effectiveness. The traditional relative contribution rate method poses limitations by focusing on individual equipment evaluation without considering the interrelations between equipment. In response to the issue, this study proposes a framework based on operation loop and improved Shapley value (OLISV) for analysis to ananlyze the equipment CRSoS. Specifically, a multi-layer network model is first constructed based on complex heterogeneous network and operation loop theory. Next, information entropy and evidence theory are used for the edges of the functional node layer, while improving the parallel node structure within the network. Subsequently, an improved Shapley value contribution rate method based on non-efficiency influencing factors is proposed. Finally, the rationality and effectiveness of the OLISV are illustrated through a case study.

In this paper, a three-dimension envelope-based path planning algorithm (3DE-PP) is proposed to automatically generate a collision-free trajectory for unmanned aerial vehicle (UAV). Firstly, focusing on the defects of low efficiency of obstacle modelling representation and large search space, an elliptical envelope-based obstacle modelling method is proposed to facilitate the generation of obstacle avoidance waypoints and improve the search efficiency. Then, considering safety and aiming at minimum energy consumption, waypoint generation strategies based on tangent guidance and minimum deviation are designed. Meanwhile, aiming at the UAV motion constraint, a three-dimension (3D) path construction method based on improved Dubins is proposed. Finally, combined with the main path generation algorithm based on saving algorithm, a safe and feasible 3D flight path is constructed by considering the power constraint of UAV and the access of charging stations comprehensively. The proposed 3DE-PP is compared with four algorithms (SAS, Dubins-RRT*, APF, 3D-TG) by 15 examples generated from five typical environments, and the computational results confirm its advantages. Furthermore, a real-world case is introduced, and the key factors influencing path planning are analyzed.

Forest fires are characterized by their abrupt onset and highly destructive nature, resulting in significant annual property losses. Hence, regular surveillance is imperative for forest fire prevention and mitigation. The fundamental challenge in patrolling is akin to the problem of helicopter route planning. Conventional unmanned aerial vehicle (UAV) path planning commonly entails single-trip missions. Considering the extensive and complex forest environments, we advocate a multi-stage UAV reconnaissance strategy to address the daily inspection route planning conundrum. This approach facilitates UAVs to conduct round-trip flights between designated surveillance points and the base station at diverse time intervals, effectively satisfying the requirements for multi-tiered, hierarchical reconnaissance. Furthermore, we develop an advanced multi-strategy genetic algorithm (MSGA) to optimize the multi-stage reconnaissance model. Experimental outcomes underscore the superior performance of the enhanced MSGA, achieving a reduction of nearly 20% in total flight path length relative to the traditional genetic algorithm. This methodology significantly enhances the efficacy of daily forest patrols.