In order to obtain better inverse synthetic aperture radar (ISAR) image, a novel structure-enhanced spatial spectrum is proposed for estimating the incoherence parameters and fusing multiband. The proposed method takes full advantage of the original electromagnetic scattering data and its conjugated form by combining them with the novel covariance matrices. To analyse the superiority of the modified algorithm, the mathematical expression of equivalent signal to noise ratio (SNR) is derived, which can validate our proposed algorithm theoretically. In addition, compared with the conventional matrix pencil (MP) algorithm and the conventional root-multiple signal classification (Root-MUSIC) algorithm, the proposed algorithm has better parameter estimation performance and more accurate multiband fusion results at the same SNR situations. Validity and effectiveness of the proposed algorithm is demonstrated by simulation data and real radar data.

Solar radio burst (SRB) is one of the main natural interference sources of Global Positioning System (GPS) signals and can reduce the signal-to-noise ratio (SNR), directly affecting the tracking performance of GPS receivers. In this paper, a tracking algorithm based on the adaptive Kalman filter (AKF) with carrier-to-noise ratio estimation is proposed and compared with the conventional second-order phase-locked loop tracking algorithms and the improved Sage-Husa adaptive Kalman filter (SHAKF) algorithm. It is discovered that when the SRBs occur, the improved SHAKF and the AKF with carrier-to-noise ratio estimation enable stable tracking to loop signals. The conventional second-order phase-locked loop tracking algorithms fail to track the receiver signal. The standard deviation of the carrier phase error of the AKF with carrier-to-noise ratio estimation outperforms 50.51% of the improved SHAKF algorithm, showing less fluctuation and better stability. The proposed algorithm is proven to show more excellent adaptability in the severe environment caused by the SRB occurrence and has better tracking performance.

Ant colony optimization (ACO) is a random search algorithm based on probability calculation. However, the uninformed search strategy has a slow convergence speed. The Bayesian algorithm uses the historical information of the searched point to determine the next search point during the search process, reducing the uncertainty in the random search process. Due to the ability of the Bayesian algorithm to reduce uncertainty, a Bayesian ACO algorithm is proposed in this paper to increase the convergence speed of the conventional ACO algorithm for image edge detection. In addition, this paper has the following two innovations on the basis of the classical algorithm, one of which is to add random perturbations after completing the pheromone update. The second is the use of adaptive pheromone heuristics. Experimental results illustrate that the proposed Bayesian ACO algorithm has faster convergence and higher precision and recall than the traditional ant colony algorithm, due to the improvement of the pheromone utilization rate. Moreover, Bayesian ACO algorithm outperforms the other comparative methods in edge detection task.

The fifth-generation (5G) communication requires a highly accurate estimation of the channel state information (CSI) to take advantage of the massive multiple-input multiple-output (MIMO) system. However, traditional channel estimation methods do not always yield reliable estimates. The methodology of this paper consists of deep residual shrinkage network (DRSN) neural network-based method that is used to solve this problem. Thus, the channel estimation approach, based on DRSN with its learning ability of noise-containing data, is first introduced. Then, the DRSN is used to train the noise reduction process based on the results of the least square (LS) channel estimation while applying the pilot frequency subcarriers, where the initially estimated subcarrier channel matrix is considered as a three-dimensional tensor of the DRSN input. Afterward, a mixed signal to noise ratio (SNR) training data strategy is proposed based on the learning ability of DRSN under different SNRs. Moreover, a joint mixed scenario training strategy is carried out to test the multi scenarios robustness of DRSN. As for the findings, the numerical results indicate that the DRSN method outperforms the spatial-frequency-temporal convolutional neural networks (SF-CNN) with similar computational complexity and achieves better advantages in the full SNR range than the minimum mean squared error (MMSE) estimator with a limited dataset. Moreover, the DRSN approach shows robustness in different propagation environments.

This paper considers the short-range sensing implementation in continuous-wave (CW) phased array systems. We specifically address this CW short-range sensing challenges stemming from the self-interference cancellation (SIC) operation and synthesis requirement of arbitrary beampatterns for the sensing purpose, which has rarely been researched before. In this paper, unlike the only existed work that exploits the heuristic method and shares no analytical solution, an SIC pattern synthesis design is presented with a closed-form solution. By utilizing the null-space projection (NSP) method, the proposed method effectively mitigates the self-interference to enable the in-band full-duplex operation of the array system. Subsequently, the NSP design will be innovatively embedded in a singular value decomposition (SVD) based weighted alternating reserve projection (WARP) approach to efficiently synthesize an arbitrary desired pattern by solving a unique rank-deficient weighted least mean square problem. Numerical results validate the effectiveness of the proposed method in terms of beampattern, SIC performance, and sensing performance.

In low Earth orbit (LEO) satellite networks, on-board energy resources of each satellite are extremely limited. And with the increase of the node number and the traffic transmission pressure, the energy consumption in the networks presents uneven distribution. To achieve energy balance in networks, an energy consumption balancing optimization algorithm of LEO networks based on distance energy factor (DEF) is proposed. The DEF is defined as the function of the inter-satellite link distance and the cumulative network energy consumption ratio. According to the minimum sum of DEF on inter-satellite links, an energy consumption balancing algorithm based on DEF is proposed, which can realize dynamic traffic transmission optimization of multiple traffic services. It can effectively reduce the energy consumption pressure of core nodes with high energy consumption in the network, make full use of idle nodes with low energy consumption, and optimize the energy consumption distribution of the whole network according to the continuous iterations of each traffic service flow. Simulation results show that, compared with the traditional shortest path algorithm, the proposed method can improve the balancing performance of nodes by 75% under certain traffic pressure, and realize the optimization of energy consumption balancing of the whole network.

In this paper, we propose an improved YOLOv5-based object detection method for radar images, which have the characteristics of diffuse weak noise and imaging distortion. To mitigate the effects of noise without losing spatial information, an coordinate attention (CA) has been added to pre-extract the feature of the images, which can guarantee a better feature extraction ability. A new stochastic weighted average (SWA) method is designed to refine generalization ability of the algorithm, where the medium mean is used instead of their average value. By introducing an deformable convolution, both regular and irregular images can be proceeded. The experimental results show that the improved algorithm performs better in object detection of radar images compared with the YOLOv5 model, which confirms the effectiveness and feasibility of our model.

Nonperiodic interrupted sampling repeater jamming (ISRJ) against inverse synthetic aperture radar (ISAR) can obtain two-dimensional blanket jamming performance by joint fast and slow time domain interrupted modulation, which is obviously different from the conventional multi-false-target deception jamming. In this paper, a suppression method against this kind of novel jamming is proposed based on inter-pulse energy function and compressed sensing theory. By utilizing the discontinuous property of the jamming in slow time domain, the unjammed pulse is separated using the intra-pulse energy function difference. Based on this, the two-dimensional orthogonal matching pursuit (2D-OMP) algorithm is proposed. Further, it is proposed to reconstruct the ISAR image with the obtained unjammed pulse sequence. The validity of the proposed method is demonstrated via the Yake-42 plane data simulations.

The multifunctional integration system (MFIS) is based on a common hardware platform that controls and regulates the system’s configurable parameters through software to meet different operational requirements. Dwell scheduling is a key for the system to realize multifunction and maximize the resource utilization. In this paper, an adaptive dwell scheduling optimization model for MFIS which considers the aperture partition and joint radar communication (JRC) waveform is established. To solve the formulated optimization problem, JRC scheduling conditions are proposed, including time overlapping condition, beam direction condition and aperture condition. Meanwhile, an effective mechanism to dynamically occupy and release the aperture resource is introduced, where the time-pointer will slide to the earliest ending time of all currently scheduled tasks so that the occupied aperture resource can be released timely. Based on them, an adaptive dwell scheduling algorithm for MFIS with aperture partition and JRC waveform is put forward. Simulation results demonstrate that the proposed algorithm has better comprehensive scheduling performance than up-to-date algorithms in all considered metrics.

The operational readiness test (ORT), like weapon testing before firing, is becoming more and more important for systems used in the field. However, the test requirement of the ORT is distinctive. Specifically, the rule of selecting test items should be changed in different test turns, and whether there is a fault is more important than where the fault is. The popular dependency matrix (D-matrix) processing algorithms becomes low efficient because they cannot change their optimizing direction and spend unnecessary time on fault localization and isolation. To this end, this paper proposes a D-matrix processing algorithm named piecewise heuristic algorithm for D-matrix (PHAD). Its key idea is to use a piecewise function comprised of multiple different functions instead of the commonly used fixed function and switch subfunctions according to the test stage. In this manner, PHAD has the capability of changing optimizing direction, precisely matching the variant test requirements, and generating an efficient test sequence. The experiments on the random matrixes of different sizes and densities prove that the proposed algorithm performs better than the classical algorithms in terms of expected test cost (ETC) and other metrics. More generally, the piecewise heuristic function shows a new way to design D-matrix processing algorithm and a more flexible heuristic function to meet more complicated test requirements.

High complexity and uncertainty of air combat pose significant challenges to target intention prediction. Current interpolation methods for data pre-processing and wrangling have limitations in capturing interrelationships among intricate variable patterns. Accordingly, this study proposes a Mogrifier gate recurrent unit-D (Mog-GRU-D) model to address the combat target intention prediction issue under the incomplete information condition. The proposed model directly processes missing data while reducing the independence between inputs and output states. A total of 1200 samples from twelve continuous moments are captured through the combat simulation system, each of which consists of seven dimensional features. To benchmark the experiment, a missing valued dataset has been generated by randomly removing 20% of the original data. Extensive experiments demonstrate that the proposed model obtains the state-of-the-art performance with an accuracy of 73.25% when dealing with incomplete information. This study provides possible interpretations for the principle of target interactive mechanism, highlighting the model’s effectiveness in potential air warfare implementation.

The dwell scheduling problem for a multifunctional radar system is led to the formation of corresponding optimization problem. In order to solve the resulting optimization problem, the dwell scheduling process in a scheduling interval (SI) is formulated as a Markov decision process (MDP), where the state, action, and reward are specified for this dwell scheduling problem. Specially, the action is defined as scheduling the task on the left side, right side or in the middle of the radar idle timeline, which reduces the action space effectively and accelerates the convergence of the training. Through the above process, a model-free reinforcement learning framework is established. Then, an adaptive dwell scheduling method based on Q-learning is proposed, where the converged Q value table after training is utilized to instruct the scheduling process. Simulation results demonstrate that compared with existing dwell scheduling algorithms, the proposed one can achieve better scheduling performance considering the urgency criterion, the importance criterion and the desired execution time criterion comprehensively. The average running time shows the proposed algorithm has real-time performance.

Project construction and development are an important part of future army designs. In today’s world, intelligent warfare and joint operations have become the dominant developments in warfare, so the construction and development of the army need top-down, top-level design, and comprehensive planning. The traditional project development model is no longer sufficient to meet the army’s complex capability requirements. Projects in various fields need to be developed and coordinated to form a joint force and improve the army’s combat effectiveness. At the same time, when a program consists of large-scale project data, the effectiveness of the traditional, precise mathematical planning method is greatly reduced because it is time-consuming, costly, and impractical. To solve above problems, this paper proposes a multi-stage program optimization model based on a heterogeneous network and hybrid genetic algorithm and verifies the effectiveness and feasibility of the model and algorithm through an example. The results show that the hybrid algorithm proposed in this paper is better than the existing meta-heuristic algorithm.

To address the confrontation decision-making issues in multi-round air combat, a dynamic game decision method is proposed based on decision tree for the confrontation of unmanned aerial vehicle (UAV) air combat. Based on game theory and the confrontation characteristics of air combat, a dynamic game process is constructed including the strategy sets, the situation information, and the maneuver decisions for both sides of air combat. By analyzing the UAV’s flight dynamics and the both sides’ information, a payment matrix is established through the situation advantage function, performance advantage function, and profit function. Furthermore, the dynamic game decision problem is solved based on the linear induction method to obtain the Nash equilibrium solution, where the decision tree method is introduced to obtain the optimal maneuver decision, thereby improving the situation advantage in the next round of confrontation. According to the analysis, the simulation results for the confrontation scenarios of multi-round air combat are presented to verify the effectiveness and advantages of the proposed method.

This paper proposes a reliability evaluation model for a multi-dimensional network system, which has potential to be applied to the internet of things or other practical networks. A multi-dimensional network system with one source element and multiple sink elements is considered first. Each element can connect with other elements within a stochastic connection ranges. The system is regarded as successful as long as the source element remains connected with all sink elements. An importance measure is proposed to evaluate the performance of non-source elements. Furthermore, to calculate the system reliability and the element importance measure, a multi-valued decision diagram based approach is structured and its complexity is analyzed. Finally, a numerical example about the signal transfer station system is illustrated to analyze the system reliability and the element importance measure.

As commercial drone delivery becomes increasingly popular, the extension of the vehicle routing problem with drones (VRPD) is emerging as an optimization problem of interests. This paper studies a variant of VRPD in multi-trip and multi-drop (VRP-mmD). The problem aims at making schedules for the trucks and drones such that the total travel time is minimized. This paper formulate the problem with a mixed integer programming model and propose a two-phase algorithm, i.e., a parallel route construction heuristic (PRCH) for the first phase and an adaptive neighbor searching heuristic (ANSH) for the second phase. The PRCH generates an initial solution by concurrently assigning as many nodes as possible to the truck–drone pair to progressively reduce the waiting time at the rendezvous node in the first phase. Then the ANSH improves the initial solution by adaptively exploring the neighborhoods in the second phase. Numerical tests on some benchmark data are conducted to verify the performance of the algorithm. The results show that the proposed algorithm can found better solutions than some state-of-the-art methods for all instances. Moreover, an extensive analysis highlights the stability of the proposed algorithm.

This survey presents a comprehensive review of various methods and algorithms related to passing-through control of multi-robot systems in cluttered environments. Numerous studies have investigated this area, and we identify several avenues for enhancing existing methods. This survey describes some models of robots and commonly considered control objectives, followed by an in-depth analysis of four types of algorithms that can be employed for passing-through control: leader-follower formation control, multi-robot trajectory planning, control-based methods, and virtual tube planning and control. Furthermore, we conduct a comparative analysis of these techniques and provide some subjective and general evaluations.

This paper presents a quadcopter system for navigation in outdoor urban environments. The main contributions include the hardware design, the establishment of global occupancy grid maps based on millimeter-wave radars, the trajectory planning scheme based on optimal virtual tube methods, and the controller structure based on dynamics. The proposed system focuses on utilizing a compact and lightweight quadrotor with sensors to achieve navigation that conforms to the direction of urban roads with high computational efficiency and safety. Our work is an application of millimeter-wave radars and virtual tube planning for obstacle avoidance in navigation. The validness and effectiveness of the proposed system are verified by experiments.

How multi-unmanned aerial vehicles (UAVs) carrying a payload pass an obstacle-dense environment is practically important. Up to now, there have been few results on safe motion planning for the multi-UAVs cooperative transportation system (CTS) to pass through such an environment. The problem is challenging because it is difficult to analyze and explicitly take into account the swing motion of the payload in planning. In this paper, a modeling method of virtual tube is proposed by fusing the advantages of the existing modeling algorithm for regular virtual tube and the expansion environment method. The proposed method can not only generate a safe and smooth tube for UAVs, but also ensure the payload stays away from the dense obstacles. Simulation results show the effectiveness of the method and the safety of the planned tube.

Developing intelligent unmanned swarm systems (IUSSs) is a highly intricate process. Although current simulators and toolchains have made a notable contribution to the development of algorithms for IUSSs, they tend to concentrate on isolated technical elements and are deficient in addressing the full spectrum of critical technologies and development needs in a systematic and integrative manner. Furthermore, the current suite of tools has not adequately addressed the challenge of bridging the gap between simulation and real-world deployment of algorithms. Therefore, a comprehensive solution must be developed that encompasses the entire IUSS development lifecycle. In this study, we present the RflySim ToolChain, which has been developed with the specific aim of facilitating the rapid development and validation of IUSSs. The RflySim ToolChain employs a model-based design (MBD) approach, integrating a modeling and simulation module, a lower reliable control module, and an upper swarm decision-making module. This comprehensive integration encompasses the entire process, from modeling and simulation to testing and deployment, thereby enabling users to rapidly construct and validate IUSSs. The principal advantages of the RflySim ToolChain are as follows: it provides a comprehensive solution that meets the full-stack development needs of IUSSs; the highly modular architecture and comprehensive software development kit (SDK) facilitate the automation of the entire IUSS development process. Furthermore, the high-fidelity model design and reliable architecture solution ensure a seamless transition from simulation to real-world deployment, which is known as the simulation to reality (Sim2Real) process. This paper presents a series of case studies that illustrate the effectiveness of the RflySim ToolChain in supporting the research and application of IUSSs.

This paper presents a method of multicopter interception control based on visual servo and virtual tube in a cluttered environment. The proposed hybrid heuristic function improves the efficiency of the A* algorithm. The revised objective function makes the virtual tube generating curve not only smooth but also close to the path points generated by the A* algorithm. In six different simulation scenarios, the efficiency of the modified A* algorithm is 6.2% higher than that of the traditional A* algorithm. The efficiency of path planning and virtual tube planning is verified by simulations. The effectiveness of interception control is verified by a software-in-loop (SIL) simulation.

Unmanned aerial vehicles (UAVs) have become one of the key technologies to achieve future data collection due to their high mobility, rapid deployment, low cost, and the ability to establish line-of-sight communication links. However, when UAV swarm perform tasks in narrow spaces, they often encounter various spatial obstacles, building shielding materials, and high-speed node movements, which result in intermittent network communication links and cannot support the smooth completion of tasks. In this paper, a high mobility and dynamic topology of the UAV swarm is particularly considered and the high dynamic mobile topology-based clustering (HDMTC) algorithm is proposed. Simulation and real flight verification results verify that the proposed HDMTC algorithm achieves higher stability of network, longer link expiration time (LET), and longer node lifetime, all of which improve the communication performance for UAV swarm networks.