Complex Battlefield Environment Research Articles

Radar antenna scanning type identification based on the fusion of multiple temporal features

Identifying the antenna scanning type (AST) of radar signal aims to deeply analyze the parameters of radar waveform and accurately judge the AST of the radar signal. Traditional methods rely heavily on domain priors and expert experience to set classification thresholds, which introduces human errors. Moreover, the generalization and anti-noise interference abilities are relatively weak, and it is difficult to deal with complex battlefield environments. The model based on the convolutional neural network (CNN) is limited by the receptive field of the convolution kernel, which makes the model unable to make full use of the global information of the signal. To fully exploit the global timing characteristics of radar signals, we propose a radar AST recognition method based on the fusion of multiple temporal features. Specifically, for the time series characteristics of the radar antenna scanning signal, a 1D CNN branch is first built to extract its short time series features. Given the limited receptive field of the convolutional network and the inability to fully consider the overall characteristics of the radar signal, we propose a transformer network branch to extract the global timing features fully. The recognition of radar AST based on multi-time series feature fusion can fully model the local and global sequence attributes, thereby improving the cognitive recognition ability of radar signals. Our method attains state-of-the-art results based on experimental findings.

A New Multi-Target Three-Way Threat Assessment Method with Heterogeneous Information and Attribute Relevance

Target threat assessment provides support for combat decision making. The multi-target threat assessment method based on a three-way decision can obtain threat classification while receiving threat ranking, thus avoiding the limitation of traditional two-way decisions. However, the heterogeneous situation information, attribute relevance, and adaptive information processing needs in complex battlefield environment bring challenges to existing methods. Therefore, this paper proposes a new multi-target three-way threat assessment method with heterogeneous information and attribute relevance. Firstly, dynamic assessment information is represented by heterogeneous information, and attribute weights are calculated by heterogeneous Criteria Importance Through Intercriteria Correlation (CRITIC). Then, the conditional probability is calculated by the heterogeneous weighted Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and the adaptive risk avoidance coefficients are constructed by calculating the uncertainty of the assessment value, and then the relative loss function matrices are constructed. Finally, the comprehensive loss function matrices are obtained by the weighted Heronian mean (HM) operator, and the comprehensive thresholds are calculated to obtain the three-way rules. The case study shows that compared with the existing methods, the proposed method can effectively handle the heterogeneous information and attribute relevance, and obtain the risk avoidance coefficients without presetting or field subjective settings, which is more suitable for the complex mission environment.

Improved Deep Residual Shrinkage Network for Intelligent Interference Recognition with Unknown Interference.

In complex battlefield environments, flying ad-hoc network (FANET) faces challenges in manually extracting communication interference signal features, a low recognition rate in strong noise environments, and an inability to recognize unknown interference types. To solve these problems, one simple non-local correction shrinkage (SNCS) module is constructed. The SNCS module modifies the soft threshold function in the traditional denoising method and embeds it into the neural network, so that the threshold can be adjusted adaptively. Local importance-based pooling (LIP) is introduced to enhance the useful features of interference signals and reduce noise in the downsampling process. Moreover, the joint loss function is constructed by combining the cross-entropy loss and center loss to jointly train the model. To distinguish unknown class interference signals, the acceptance factor is proposed. Meanwhile, the acceptance factor-based unknown class recognition simplified non-local residual shrinkage network (AFUCR-SNRSN) model with the capacity for both known and unknown class recognition is constructed by combining AFUCR and SNRSN. Experimental results show that the recognition accuracy of the AFUCR-SNRSN model is the highest in the scenario of a low jamming to noise ratio (JNR). The accuracy is increased by approximately 4-9% compared with other methods on known class interference signal datasets, and the recognition accuracy reaches 99% when the JNR is -6 dB. At the same time, compared with other methods, the false positive rate (FPR) in recognizing unknown class interference signals drops to 9%.

A decentralized decision-making algorithm of UAV swarm with information fusion strategy

Centralized and decentralized unmanned aerial vehicle (UAV) swarms rely on networked communication to exchange information among individuals and generate cooperative behaviors. This study proposes an Information-Fusion based Decentralized Swarm Decision Algorithm (IFDSDA) to coordinate UAV swarms when communication is interfered with or fails. Each UAV has the ability to perceive the area ahead using a monocular camera. An information fusion strategy is presented to unify the two types of information obtained from communication and visual perception in a standardized manner. This mechanism enables UAVs to fully utilize different information in cases of communication failure. Each UAV is controlled by a decentralized swarm decision module that takes the fused information as inputs and generates the heading orientation by combining basic action rules of UAVs. The weight parameters of the combination are optimized using the heuristic genetic algorithm in an offline manner. The collision/obstacle avoidance and area search missions are used as test scenarios for the proposed IFDSDA. Simulations with high confidence are conducted, which demonstrate the effectiveness, inherent scalability, and robustness of the proposed method, comparing it with the state-of-the-art ISOA method and its variant. This study reduces the dependence on network communication within the swarm and improves the adaptability of UAV swarms in complex battlefield environments.

Threat assessment based on single-valued neutrosophic TOPSIS and three-way decision

This paper provides a novel target threat assessment model that utilizes TOPSIS and three-way decision-making under a single-valued neutrosophic environment. The presented model provides theoretical support for combat decision-making in complex battlefield environments with uncertain information. The model employs single-valued neutrosophic sets to handle uncertain data, which enhances the descriptive ability of information. The maximum deviation method is used to calculate attribute weight factors, which highlights the importance of each attribute. The final target threat ranking is obtained based on the relative closeness coefficient of each target. Furthermore, the proposed model constructs a multi-attribute aggregation loss function matrix for each target, sets the risk avoidance coefficient under the knowledge of the battlefield condition, and calculates the decision threshold of each target using three-way decision theory. This method produces the classification of the target choice. The numerical examples and comparison analysis demonstrate that the suggested model can handle ambiguous scenario information effectively and reasonably, transform traditional decision-making ranking results into three-way classification findings, and provide a rationale for choosing an attacking target.

Integrated fire/flight control coupler design for armed helicopters based on improved non-monotone adaptive trust region algorithm with distributionally robust optimization

Aiming at enhancing the extent of military automatization and the effectiveness of armed helicopters in complex battlefield environment, integrated fire/flight control (IFFC) coupler connects the fire control system and the flight control system which is used to generate the optimal flight command signals. However, it is difficult to obtain satisfactory flight command signals within a certain firing accuracy due to the unavoidable uncertain information in battlefield environment. In this paper, a new IFFC scheme is proposed for armed helicopters against complex combat by using the idea of distributionally robust optimization combined with the framework of trust region. Initially, a vector equation and coordinate transformation are employed to acquire the relative motion model of the IFFC coupler problem in complex battlefield environment. Moreover, an improved performance index is designed according to the constraints, and the original non-convex problem is handled by the Taylor formula combined with an improved BFGS (Broyden–Fletcher–Goldfard–Shanno) algorithm. Considering the uncertain parameters during optimization, the non-linear distributionally robust optimization is transformed into a linear matrix inequality optimization problem based on the worst-case conditional value-at-risk (WC-CVaR) approximation. Then, an algorithmic framework with a non-monotone adaptive trust region algorithm is given to obtain the optimal flight command signals. Finally, a simulation example is given to illustrate the effectiveness of the proposed approach.

Deep learning-based laser and infrared composite imaging for armor target identification and segmentation in complex battlefield environments

In this paper, a novel method of armor target recognition and segmentation in complex battlefield environments is proposed. First, distance image and infrared image data of armored targets are acquired using LIDAR and IR detectors, and the data are fused based on improved guided filtering to increase the diversity of input features. Secondly, the experimental dataset is expanded using improved GAN to generate simulated data onto different features. Then, effective recognition of armored targets is achieved by adding a CBAM attention mechanism to the YOLOv5 target detection algorithm. Finally, by improving the Deeplabv3 + semantic segmentation algorithm, which mainly lies in adding the CBAM attention mechanism and lightening the algorithm, the segmentation accuracy is improved while improving the real-time performance of detection. The experimental results show that the algorithm proposed in this paper has higher accuracy and real-time performance than the experimental comparison algorithm, and the mean average precision (mAP) can reach more than 99.64 %. And the algorithm can accurately segment the armored targets’ combat area, and the mean pixel accuracy (MPA) can reach more than 96.57 %. It is concluded that the algorithm significantly improves the effective identification and precise strike capability of armored targets in complex battlefield environments.

Optimal trajectory planning technology for the cooperative flight of unmanned aerial vehicles

In the face of a complex battlefield environment, a multi-dimensional dynamic planning algorithm is proposed to solve the trajectory planning problem of the cooperative flight mission process of unmanned aerial vehicles. Under the constraints of aircraft kinematics, the collision between aircraft and space-time cooperation, and under the influence of random wind field, threat area, and other interference factors, the algorithm can make the UAV group pass through the mission points as much as possible, and at the same time, smooth the track points to make the trajectory reach the optimal solution. Finally, the simulation results of the program written in C++ and MATLAB show that the algorithm can meet the global optimization, and can calculate in real-time and online. It has strong engineering application value.

A Spider Monkey Optimization Based on Beta-Hill Climbing Optimizer for Unmanned Combat Aerial Vehicle (UCAV)

Unmanned Combat Aerial Vehicle (UCAV) path planning is a challenging optimization problem that seeks the optimal or near-optimal flight path for military operations. The problem is further complicated by the need to operate in a complex battlefield environment with minimal military risk and fewer constraints. To address these challenges, highly sophisticated control methods are required, and Swarm Intelligence (SI) algorithms have proven to be one of the most effective approaches. In this context, a study has been conducted to improve the existing Spider Monkey Optimization (SMO) algorithm by integrating a new explorative local search algorithm called Beta-Hill Climbing Optimizer (BHC) into the three main phases of SMO. The result is a novel SMO variant called SMOBHC, which offers improved performance in terms of intensification, exploration, avoiding local minima, and convergence speed. Specifically, BHC is integrated into the main SMO algorithmic structure for three purposes: to improve the new Spider Monkey solution generated in the SMO Local Leader Phase (LLP), to enhance the new Spider Monkey solution produced in the SMO Global Leader Phase (GLP), and to update the positions of all Local Leader members of each local group under a specific condition in the SMO Local Leader Decision (LLD) phase. To demonstrate the effectiveness of the proposed algorithm, SMOBHC is applied to UCAV path planning in 2D space on three different complex battlefields with ten, thirty, and twenty randomly distributed threats under various conditions. Experimental results show that SMOBHC outperforms the original SMO algorithm and a large set of twenty-six powerful and recent evolutionary algorithms. The proposed method shows better results in terms of the best, worst, mean, and standard deviation outcomes obtained from twenty independent runs on small-scale (D = 30), medium-scale (D = 60), and large-scale (D = 90) battlefields. Statistically, SMOBHC performs better on the three battlefields, except in the case of SMO, where there is no significant difference between them. Overall, the proposed SMO variant significantly improves the obstacle avoidance capability of the SMO algorithm and enhances the stability of the final results. The study provides an effective approach to UCAV path planning that can be useful in military operations with complex battlefield environments.

Manoeuvre decision‐making of unmanned aerial vehicles in air combat based on an expert actor‐based soft actor critic algorithm

AbstractThe demand for autonomous motion control of unmanned aerial vehicles in air combat is boosted as taking the initiative in combat appears more and more crucial. Unmanned aerial vehicles inability to manoeuvre autonomously during air combat that features highly dynamic and uncertain manoeuvres of the enemy; however, limits their combat capabilities, which proves to be very challenging. To meet the challenge, this article proposes an autonomous manoeuvre decision model using an expert actor‐based soft actor critic algorithm that reconstructs empirical replay buffer with expert experience. Specifically, the algorithm uses a small amount of expert experience to increase the diversity of the samples, which can largely improve the exploration and utilisation efficiency of deep reinforcement learning. And to simulate the complex battlefield environment, a one‐to‐one air combat model is established and the concept of missile's attack region is introduced. The model enables the one‐to‐one air combat to be simulated under different initial battlefield situations. Simulation results show that the expert actor‐based soft actor critic algorithm can find the most favourable policy for unmanned aerial vehicles to defeat the opponent faster, and converge more quickly, compared with the soft actor critic algorithm.

Simulation Analysis of the Safety of High-Energy Hydroxyl-Terminated Polybutadiene (HTPB) Engine under the Impact of Fragments

The safety of solid rocket engine use seriously affects the survivability and combat effectiveness of weaponries. To study the engine safety against fragment in complex battlefield environments, the fragment impact safety simulation study of a high-energy four-component HTPB propellant solid engine (hereafter referred to as high-energy HTPB propellant engine) was conducted. The equation of state parameters and reaction rate equation parameters of the detonation product of high-energy HTPB propellant were calibrated by using a 50 mm diameter cylinder test and Lagrange test combined with genetic algorithm. The nonlinear dynamics software LS-DYNA was used to build a finite element model of the fragment impact engine and simulate the mechanical response of the high-energy HTPB propellant under different operating conditions. This study shows that the critical detonation velocity decreases with the increase of the number of fragments. When the number of fragments is more than 5, the influence of this factor on the critical detonation velocity is no longer obvious. Under the same loading strength conditions, the greater the metal shell strength and the greater the shell wall thickness, the more difficult it is for the high-energy HTPB propellant to be detonated by the shock. This study can provide a reference for the design and optimization analysis of solid rocket engine fragment impact safety.

Research on Lazy Theta* Route Planning Algorithm Based on Grid Point Optimization

In recent years, the problem of route planning in complex battlefield environments has attracted significant attention. With the increasingly worrying international situation, safety and flyability in a continuously changing threat environment are critical factors in route planning research. Thus, this paper proposes an improved Lazy Theta* algorithm that adapts to a complex battlefield environment and finds the optimal route. Specifically, given the low computational efficiency and data redundancy of the existing environmental threat modeling, the developed scheme first employs an octree grid to divide the environment into a grid. Furthermore, based on a real environmental threat model and flight constraints, we design a Lazy Theta* algorithm based on octree grid points, which shortens the planning path and reduces the path cost. Finally, this paper proposes an equally spaced B-spline to smooth the route and improve its smoothness and flyability. Several simulated experiments verify that the smoothed route improves safety and flight ability while reducing the route’s distance. Overall, the simulation results prove that the proposed method significantly improves the planning efficiency and flyability compared with traditional methods.

A double-layer coding model with a rotation-based particle swarm algorithm for unmanned combat aerial vehicle path planning

Unmanned combat aerial vehicle (UCAV) technology has to address many challenges in complex battlefield environments. To produce a safe and low-energy flying path, a UCAV requires many points to build a path that avoids threats, which increases the problem dimension, consumes more computational resources and makes the results unstable. To address this issue, this paper employs a new model known as double-layer coding (DLC) for path planning with unevenly distributed points to decrease the number of superfluous points on the path. Meanwhile, the RPSO algorithm, which introduces a novel strategy of rotating particles in high-dimensional space to search for targets, is proposed as an enhanced particle swarm optimization (PSO) algorithm. The proposed method effectively improves PSO′s exploration capacity. Furthermore, RPSO is employed to implement the double-layer coding model for path planning (DLCRPSO). The experimental results show that the proposed DLCRPSO method for path planning always produces feasible flight paths in complex environments.

GCD-YOLOv5: An Armored Target Recognition Algorithm in Complex Environments Based on Array Lidar

For the recognition of armored targets in complex battlefield environments, how to reduce missed and false alarms while achieving real-time is an urgent issue. To this end, the GCD-YOLOv5 algorithm is innovatively proposed. Firstly, array lidar is used to acquire the armor target data. Secondly, the armor target data is expanded with an improved GAN(Generative Adversarial Network) to increase the diversity of training data. Afterward, the expanded dataset is fed into the GCD-YOLv5(You Only Look Once) for training. And the GCD-YOLOv5 is reflected in the following aspects. Firstly, the CBAM(Convolutional Block Attention Module) and the multi-scale feature fusion are added to improve the feature extraction capability and detection efficiency, increasing the recognition capability of small and obscured targets. Secondly, combining with DETR(Detection Transformer) to lighten YOLOv5 to achieve the real-time requirement. Thirdly, the YOLOv5 loss function and prediction box filtering method are improved to increase the detection accuracy and the confidence of the detection boxes. The experimental results show that the GCD-YOLOv5 algorithm has higher accuracy and real-time, the mAP(mean Average Precision) can reach 99.7%, and fps is 68.56% higher compared to YOLOv5, which significantly improves the recognition capability of armored targets in complex battlefield environments.

Pole Extraction of Radar Target in Resonant Region Based on Sliding-Window Matrix Pencil Method

In the complex battlefield environment, stealth radar target recognition has been paid increasing attentions. Previous studies have demonstrated that the stealth target can be identified well by pole extraction based on matrix pencil method (MPM). However, MPM suffers from the difficulty in setting model order and the time-domain resonant response aliasing problem. This paper proposes a new sliding-window matrix pencil method (SW-MPM) based on dynamic order setting and sliding window. Dynamic order setting scheme is used to overcome the difficulty of setting the order in matrix pencil method, while sliding window can avoid the aliasing problem of time-domain resonant response to some extent. The time-domain scattering data used in SW-MPM are obtained by inverse Fourier transform of the frequency domain scattering data in the resonance region. Taking typical stealth aircraft identification as example, the simulation results verify that the proposed method may extract more number of poles with better azimuth consistency, which is beneficial to improve the accuracy of pole-based target identification.

Camouflage Target Segmentation Algorithm Using Multi-Scale Feature Extraction and Multi-Level Attention Mechanism

There are two problems when semantic segmentation algorithm is used in the battlefield environment: The camouflage target segmentation boundary is not ideal, and the small target segmentation accuracy is low. So a camouflage target semantic segmentation algorithm CSS-Net is proposed, which combines multi-scale feature extraction method and multi-level attention mechanism. The algorithm consistes of an encoder-decoder structure. In the encoder part, a lightweight and deep separable convolution with residual structure is used to construct the feature encoder to extract features from the reconnaissance images. In the decoder part, a strategy-selected multi-scale feature fusion module and a multi-level attention feature enhancement module are designed to obtain the multi-scale representation information and the channel information of the images. It furtherly enhances the semantic decoding process while gradually refining the segmentation results. The experiment results show that the CSS-Net algorithm can effectively complete the segmentation and recognition of camouflage targets in complex battlefield environment. The overall mIoU value reaches 91.98%, and the segmentation boundary is improved. Compared with DeepLab v3+ algorithm, the mIoU value of CSS-Net on camouflage small targets is increased by 3.71 percents, and the mIoU value on multi-scale targets exceeds 85%. The segmentation effects are improved significantly.

A Task Allocation Strategy of the UAV Swarm Based on Multi-Discrete Wolf Pack Algorithm

With the continuous development of artificial intelligence, swarm control and other technologies, the application of Unmanned Aerial Vehicles (UAVs) in the battlefield is more and more extensive, and the UAV swarm is increasingly playing a prominent role in the future of warfare. How tasks are assigned in the dynamic and complex battlefield environment is very important. This paper proposes a task assignment model and its objective function based on dynamic information convergence. In order to resolve this multidimensional function, the Wolf Pack Algorithm (WPA) is selected as the alternative optimization algorithm. This is because its functional optimization of high-dimensional complex problems is better than other intelligent algorithms, and the fact that it is more suitable for UAV swarm task allocation scenarios. Based on the traditional WPA algorithm, this paper proposes a Multi-discrete Wolf Pack Algorithm (MDWPA) to solve the UAV task assignment problem in a complex environment through the discretization of wandering, calling, sieging behavior, and new individual supplement. Through Orthogonal Experiment Design (OED) and analysis of variance, the results show that MDWPA performs with better accuracy, robustness, and convergence rate and can effectively solve the task assignment problem of UAVs in a complex dynamic environment.

A Scheme of Instantaneous Frequency Measurement with High Precision Assisted by Photonics

In modern complex battlefield environment, it is important to obtain the frequency spectrum precisely. We propose an operational method of instantaneous frequency measurement (IFM) assisted by photonics, which can achieve high precision based on stimulated Brillouin scattering (SBS), giving the credit to the narrow linewidth of gain spectrum of SBS. We use MZM and DPMZM cascade to generate a tunable continuous optical signal and modulate the measured signal to the continuous

Determining safe flight area of UAVs based on variable weight threat assessment

To manage the complexity and uncertainty faced by an unmanned aerial vehicle (UAV) and identify a safe flight area in a battlefield environment, we propose a method for determining the safe flight area of the UAV based on the variable weight theory and a Markov model. In the threat calculation, the variable weight theory is introduced, so that the weight can be adjusted according to the position of the UAV relative to the threat area. Then, the Markov model is used to predict the environmental threat the UAV may encounter during its flight. Then, a fuzzy evaluation method is used to determine the threat level in the UAV flight area, and a cloud model is introduced to construct a membership function that improves the reliability of the result. Finally, the safe flight area of the drone is determined based on the threat threshold. The simulation results show that the proposed method for determining the safe flight area of the UAV is effective in a complex battlefield environment.

Craniocerebral Dynamic Response and Cumulative Effect of Damage Under Repetitive Blast.

Soldiers suffer from multiple explosions in complex battlefield environment resulting in aggravated brain injuries. At present, researches mostly focus on the damage to human body caused by single explosion. In the repetitive impact study, small animals are mainly used for related experiments to study brain nerve damage. No in-depth research has been conducted on the dynamic response and damage of human brain under repetitive explosion shock waves. Therefore, this study use the Euler-Lagrange coupling method to construct an explosion shock wave-head fluid-structure coupling model, and numerically simulated the brain dynamic response subjected to single and repetitive blast waves, obtained flow field pressure, skull stress, skull displacement, intracranial pressure to analyze the brain damage. The simulation results of 100g equivalent of TNT exploding at 1m in front of the craniocerebral show that repetitive blast increase skull stress, intracranial pressure, skull displacement, and the damage of brain tissue changes from moderate to severe. Repetitive blasts show a certain cumulative damage effect, the severity of damage caused by double blast is 122.5% of single shock, and the severity of damage caused by triple blast is 105.9% of double blast and 131.5% of single blast. The data above shows that it is necessary to reduce soldiers' exposure from repetitive blast waves.