Battlefield Situational Awareness Research Articles

TR-RAGCN-AFF-RESS: A Method for Radar Emitter Signal Sorting

Radar emitter signal sorting (RESS) is a crucial process in contemporary electronic battlefield situation awareness. Separating pulses belonging to the same radar emitter from interleaved radar pulse sequences with a lack of prior information, high density, strong overlap, and wide parameter distribution has attracted increasing attention. In order to improve the accuracy of RESS under scenarios with limited labeled samples, this paper proposes an RESS model called TR-RAGCN-AFF-RESS. This model transforms the RESS problem into a pulse-by-pulse classification task. Firstly, a novel weighted adjacency matrix construction method was proposed to characterize the structural relationships between pulse attribute parameters more accurately. Building upon this foundation, two networks were developed: a Transformer(TR)-based interleaved pulse sequence temporal feature extraction network and a residual attention graph convolutional network (RAGCN) for extracting the structural relationship features of attribute parameters. Finally, the attention feature fusion (AFF) algorithm was introduced to fully integrate the temporal features and attribute parameter structure relationship features, enhancing the richness of feature representation for the original pulses and achieving more accurate sorting results. Compared to existing deep learning-based RESS algorithms, this method does not require many labeled samples for training, making it better suited for scenarios with limited labeled sample availability. Experimental results and analysis confirm that even with only 10% of the training samples, this method achieves a sorting accuracy exceeding 93.91%, demonstrating high robustness against measurement errors, lost pulses, and spurious pulses in non-ideal conditions.

Tactical Data-Oriented Decentralized Communication Scheme in Vehicle-Assisted Networks: Challenges and Solutions

Internet of Battle Things (IoBT) devices can collect tactical data about wide battlefields. They can facilitate battlefield situational awareness in tactical operation centers (TOCs). However, IoBT devices deployed on battlefields without cellular networks cannot directly communicate with TOCs. These IoBT devices in decentralized tactical edge networks (TENs) need to connect to TOCs through tactical relay networks (TRNs). This article views the state-of-the-art TRNs and discusses the priority and challenges of vehicle-assisted TRNs (V-TRNs). To solve the remaining problems in the V-TRN application, this article presents the vehicle-assisted tactical data communication scheme (VTDCS) based on V-TRNs. VTDCS can provide reliable data aggregation in TENs, efficient data uploading from TENs to V-TRNs, and adaptive data forwarding in V-TRNs and offloading from V-TRNs to TOCs. Experiment results demonstrate that VTDCS can help TOCs obtain battlefield data more rapidly and receive urgent tactical data earlier. Finally, this article provides some suggestions for future research directions.

Toward Fast and Energy-Efficient Access to Cloudlets in Hostile Environments

Cloudlets, which refer to the edge computing services deployed at the proximity of end devices, are key providers of connectivity, storage, and computation resources to many applications. While access to cloudlets is pervasive in typical settings, it can be difficult in challenging, even hostile environments, such as military or post-disaster scenarios, featuring multi-hop communication and energy-constrained end devices. In these cases, cloudlets may have become the only equipment powerful enough to execute life-critical applications, such as battle-field situation awareness, tactic cooperation, and search-and-rescue missions. Quality of these services is greatly influenced by the minimum time that a packet can be delivered, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cloudlet access delay</i> (CAD), whose characteristics remain unknown. To address the open question of fast and efficient cloudlet access, we establish a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">packet mobility</i> model that allows CAD and energy consumption to be analyzed as a function of the initial device-cloudlet distance. We find that the expected CAD scales either linearly or quadratically under distinct types of packet mobility, and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">successful access rate</i> (SAR) can be bounded by functions of the delay constraint. Based on these findings, we develop a packet shedding algorithm that saves 24% transmission power, and reduces the average CAD by 2%, while maintaining a similar SAR in simulated cloudlet access environments.

An aerial targets grouping method based on relation network

AbstractAerial targets grouping is an important content of air battlefield situation awareness, and intelligent aerial targets grouping is an urgent need for intelligent air combat. Traditional aerial targets grouping methods have problems such as difficulty extracting air target features, difficulty designing formation similarity measures, and lack of data samples. To solve the above problems, this paper proposes a relational network based aerial targets grouping method to achieve end‐to‐end grouping under small sample conditions. Firstly, an adaptive clustering algorithm is proposed to preliminarily cluster the aerial targets in the air battlefield situation to obtain the group to be tested. Then, a representation of aerial target groups is designed, which unifies the multi‐dimensional features of the group's temporal, spatial, and attribute. Next, based on this representation, combining 3D convolutional neural networks (3DCNN) and long short‐term memory (LSTM), a neural network structure that can automatically extract multi‐dimensional features of groups is designed. After that, the relation module is used to learn the distance metric between groups. After training the relation network, the type of group to be tested can be identified, thereby achieving the goal of grouping. A dataset of 30 types of aerial target group patterns was established through simulation. The ablation experiments were conducted to verify the performance of each module, and the experimental results showed that the designed feature extraction module can effectively extract temporal sample features, while the designed relationship module performs better in extracting temporal sample information. By comparing several typical traditional aerial target grouping methods, the proposed method improves the grouping accuracy by 11.2% to 13.4%, achieving an accuracy of 91.1% in the 5‐way 1‐shot task and 95.6% in the 5‐way 5‐shot task.

A Review of Intelligent Vision Enhancement Technology for Battlefield

This paper reviews the application of intelligent vision enhancement technology in the battlefield environment and explores new research directions. This paper mainly introduces three parts. First, we introduce a solution for enhancing the battlefield situation and using head-mounted displays for soldier combat. Second, we summarize three core technologies supporting intelligent vision enhancement technology: 3D environment reconstruction, tracking and registration, and situational awareness. Third, we summarize three application directions of intelligent vision enhancement technology on the battlefield. Finally, the problems and challenges in the research work are proposed, including current issues such as accurate battlefield situational awareness by augmented reality technology, multiperson collaborative management and data flow, as well as challenges in future battlefield situation enhancement and perception. The future development trend of intelligent vision enhancement technology on the battlefield has been prospected. Two meanings of this paper are as follows: the first is to review the research status of intelligent vision enhancement technology from the technical level and identify the key technical points that may restrict development in the future; the second is to analyze the advantages and disadvantages of intelligent vision enhancement technology from the level of battlefield application and the roles of users. In addition, this paper proposes how to take the lead and take initiative in future wars.

Cooperative UAV search strategy based on DMPC-AACO algorithm in restricted communication scenarios

Improvement of integrated battlefield situational awareness in complex environments involving dynamic factors such as restricted communications and electromagnetic interference (EMI) has become a contentious research problem. In certain mission environments, due to the impact of many interference sources on real-time communication or mission requirements such as the need to implement communication regulations, the mission stages are represented as a dynamic combination of several communication-available and communication-unavailable stages. Furthermore, the data interaction between unmanned aerial vehicles (UAVs) can only be performed in specific communication-available stages. Traditional cooperative search algorithms cannot handle such situations well. To solve this problem, this study constructed a distributed model predictive control (DMPC) architecture for a collaborative control of UAVs and used the Voronoi diagram generation method to re-plan the search areas of all UAVs in real time to avoid repetition of search areas and UAV collisions while improving the search efficiency and safety factor. An attention mechanism ant-colony optimization (AACO) algorithm is proposed for UAV search-control decision planning. The search strategy is adaptively updated by introducing an attention mechanism for regular instruction information, a priori information, and emergent information of the mission to satisfy different search expectations to the maximum extent. Simulation results show that the proposed algorithm achieves better search performance than traditional algorithms in restricted communication constraint scenarios.

Classification of LoRa Signals With Real-Time Validation Using the Xilinx Radio Frequency System-on-Chip

This paper demonstrates a real-time LoRa Internet of Things (IoT) signal classification technique that runs on Xilinx Radio Frequency System-on-Chip (RFSoC) hardware. IoT signals are being used for wider arrays of applications and therefore awareness of their presence is important for cyber security and infrastructure protection as well as battlefield situational awareness. Within this research a dataset of LoRa waveforms is captured using the RFSoC which bounds the possible combinations of waveform parameters. Offline algorithms are tested against this data to evaluate how to extract the centre frequency, bandwidth and spreading factor. The algorithms are then adapted to run natively on the Xilinx RFSoC itself to enable real-time classification of waveforms from non-cooperative LoRa transmitters with a high degree of classification success.

Deep AI military staff: cooperative battlefield situation awareness for commander’s decision making

Deep AI military staff: cooperative battlefield situation awareness for commander’s decision making

Interval TOPSIS with a novel interval number comprehensive weight for threat evaluation on uncertain information

Threat evaluation (TE) is essential in battlefield situation awareness and military decision-making. The current processing methods for uncertain information are not effective enough for their excessive subjectivity and difficulty to obtain detailed information about enemy weapons. In order to optimize TE on uncertain information, an approach based on interval Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and the interval SD-G1 (SD standard deviation) method is proposed in this article. By interval SD-G1 method, interval number comprehensive weights can be calculated by combining subjective and objective weights. Specifically, the subjective weight is calculated by interval G1 method, which is an extension of G1 method into interval numbers. And the objective weight is calculated by interval SD method, which is an extension of SD method with the mean and SD of the interval array defined in this paper. Sample evaluation results show that with the interval SD-G1 method, weights of target threat attributes can be better calculated, and the approach combining interval TOPSIS and interval SD-G1 can lead to more reasonable results. Additionally, the mean and SD of interval arrays can provide a reference for other fields such as interval analysis and decision-making.

A multi-source information fusion method for ship target recognition based on Bayesian inference and evidence theory

The ship target recognition (STR) is greatly related to the battlefield situation awareness, which has recently gained prominence in the military domains. With the diversification and complexity of military missions, ship targets are mostly performed in the form of formations. Therefore, using the formation information to improve the accuracy of the ship target type recognition is worth studying. To effectively identify ship target type, we in this paper jointly consider the ship dynamic, formation, and feature information to propose a STR method based on Bayesian inference and evidence theory. Specifically, we first calculate the ship position distance matrix and the directional distance matrix with the Dynamic Time Warping (DTW) and the difference-vector algorithm taken into account. Then, we use the two distance matrices to obtain the ship formation information at different distance thresholds by the hierarchical clustering method, based on which we can infer the ship type. Thirdly, formation information and other attribute information are as nodes of the Bayesian Network (BN) to infer the ship type. Afterward, we can convert the recognition results at different thresholds into body of evidences (BOEs) as multiple information sources. Finally, we fuse the BOEs to get the final recognition. The proposed method is verified in simulation battle scenario in this paper. The simulation results demonstrate that the proposed method achieves performance superiority as compared with other ship recognition methods in terms of recognition accuracy.

Artificial intelligence technology in battlefield situation awareness

In order to make further significant researches on battlefield situation awareness, the paper summarized the challenges in the field at first, which can be listed as the following: the massive and large data makes situation understanding difficult; the rapid change of data makes situation prediction difficult; the complex and heterogeneous data makes situation display difficult. Aiming at the above problems, the paper then discussed the application of artificial intelligence technology, such as knowledge graph, natural language processing and expert system. The appropriate applyment of relevant artificial intelligence technology will greatly improve the capability of battlefield situation awareness.

Reinforcement learning based two-level control framework of UAV swarm for cooperative persistent surveillance in an unknown urban area

Persistent surveillance in a complex unknown urban area by an unmanned aerial vehicle (UAV) swarm is a low-cost, promising future application for anti-terrorism, disaster monitoring, and battlefield situational awareness. Based on over-simplified simulated surroundings and a UAV dynamic model, a few remarkable approaches have been proposed; however, they typically rely on non-sensor-based inputs and prior knowledge on the environment or targets. To overcome these limitations, based on simulated city blocks, a two-level quasi-distributed control framework is proposed for realizing the continuous control of a UAV swarm in two defined surveillance phases. With the support of a well-trained and corrected artificial neural network (ANN) in low-level UAV manoeuvre control for target homing and collision avoidance, several preliminary high-level target allocation strategies are designed for a cooperative overall objective based on the synchronization of local surveillance data. Then, via a series of numerical simulations, an optimal high-level strategy combination is identified. Finally, the surveillance performance of this strategy combination is evaluated under various swarm sizes and UAV launching patterns. The simulation results demonstrate that the proposed control framework is applicable for UAV swarm control in the persistent surveillance of unknown urban areas.

Battlefield Image Situational Awareness Application Based on Deep Learning

With the rapid development of information technology, it has become an important topic to construct a situational awareness system that can independently mine data and information as well as perceive environmental situations by using deep learning. First, this article introduced the structure of convolutional neural networks (CNN) and You Only Look Once (YOLO) model. Then, it analyzed the structure and function of battlefield situational awareness system, and concluded that: in the whole situational awareness system, the discovery, category, and location analysis of situational elements, namely object target, is the foundation and key to realize the function. On this basis, this article establishes a battlefield situational awareness model based on the YOLO model. Finally, five common objects on the battlefield (helicopter gunship, missile, tank, soldier and gun) are classified and located, respectively. The YOLO model based on CNN is used to process the input image, and then the position, category, and corresponding confidence probability of all objects in the image are obtained directly, which realizes end-to-end learning, greatly improves the speed of target detection, and lays a foundation for assessing the battlefield situation.

Hot Region Selection Based on Selective Search and Modified Fuzzy C-Means in Remote Sensing Images

Hot region selection (HRS) is usually performed for larger scenes, especially in military target detection and battlefield situational awareness applications. Many state-of-the-art methods pay more attention on object detection. Although they can detect the region of interest in one image, these images are highly affected by illumination, rotation, and scale changes, which further increases the complexity of analysis compared to those obtained using standard remote sensing platforms. The study of HRS is of great importance in the analysis of remote sensing images. The current research focuses on the specific type of object area detection. A well-developed HRS needs to have three properties: uniform highlighting of the entire HRS, well-defined boundaries, and good robustness. Motivated by these requirements, a HRS method based on the selective search method and modified fuzzy c-means (FCM) in remote sensing images is proposed to address the detection of potential hot regions in large-scale remote sensing images. First, we create a Gaussian curvature filter to preprocess large scale remote sensing images. Second, a modified FCM segmentation method is utilized to segment the image. Third, an enhanced selective search method is adopted to establish well-defined boundaries for the HRS and to improve the immunity to noise. The geographic information is presented in this phase, which is conducive to improve the detection accuracy. In the experimental section, we compare our new method with four other extraction models on three data sets. The experimental results show that compared to the other competing models, the new model better defines the hot regions and obtains more entire boundaries in terms of Overlap and mA P.

Node Design of Wild Unmanned Alert System Based on Dempster Shafer Evidence Theory

Unmanned alert system is widely used in unattended surveillance, indoor security, battlefield situational awareness and wild fortress defense. However, due to the complex geographical conditions, poor information environment and various sources of interference in the wild, the design of wild unmanned alert system is more demanding. This paper took the design of the unmanned alert system nodes as the background, designed the pyroelectric infrared sensor, the microwave induction displacement module and the flame sensor. The algorithm used the Dempster Shafer evidence theory to fuse multi-sensor information, which realized energy-saving and stability of the wild unmanned alert system. Experiment shows that the node of wild unmanned alert system has strong anti-interference ability, and it can effectively realize the alert function.

Sources of Stress and Psychological Health Outcomes Among U.S. Air Force Total Force Distributed Common Ground System Operators.

U.S. Air Force Distributed Common Ground Station (DCGS) intelligence (Intel) operators sustain 24/7 battlefield situational awareness and facilitate precision-strike operations. DCGS operations are global, synergistic, Total Force (TF) endeavors by active duty, Air National Guard (ANG) and Reserve units, providing combatant commanders with critical real-time intelligence and shaping operational and tactical decisions. Continual surveillance of this community's psychological health is important to its military leaders. This study re-examines the most frequently reported occupational stressors, as well as the prevalence of occupational burnout (i.e., high-emotional exhaustion and cynicism, and low professional efficacy), and psychological distress within this population. Active duty (n = 1717), ANG (n = 139), and Reserve (n = 173) Intel operators participated in a comprehensive, online, occupational health assessment. Results reveal that occupational stressors contributing to elevated rates of distress, regardless of TF status (i.e., low manning, long work hours, excessive workload, and organizational communication concerns) are consistent with previous research. The prevalence of high-emotional exhaustion (AD: 29%/ANG: 25%/RES: 14%) and psychological distress (AD: 19%/ANG: 17%/RES: 5%) are above estimates for other military communities. These findings combined with demographic and occupational risk factors lay the foundation for improving psychological health within this Total Force community.

Battlefield situation awareness and networking based on agent distributed computing

The battlefield environment is complex and changeable, and there are complex terrain features and bad electromagnetic communication environment. At the present stage, the battlefield reconnaissance and communication network mainly depends on the cooperation of soldiers. With the development of unmanned aerial vehicle and pilotless technology, in the background of information warfare, the use of mobile agents to complete the formation of battlefield communication networks and the battlefield situation awareness has become a new trend. The Unmanned aerial vehicles (UAVs) technology is becoming more and more mature, and its distribution, synergy, parallelism, robustness and intelligence provide the basic conditions for the construction of a battlefield self organizing network. In this paper, we use UAVs and unmanned combat vehicles to build a mobile ad hoc network that meets the conditions of the battlefield. The network can solve the problem of slow convergence or non convergence of the traditional self-organizing network without relying on the fixed basic network facilities, which has the characteristics of rapid expansion, strong destruction resistance and no centrality. So it can meet the needs of communication in the battlefield. In this process, we combine the A star algorithm with the ant colony algorithm to realize the real-time path planning in combination with the edge computing power of the agent and the battlefield situation collected by the sensor, and the battlefield aggregation and search task can be completed quickly. And according to the planned route, the route forwarding strategy under known path is used to complete the information transmission.

Effects and Modes of Air Defense Forces Operation Supported by Space Information

The paper dissects space information system composition. The paper discusses the effect of air defense forces operation supported by space information from four aspects of enhancing battlefield situational awareness, improving operational command effectiveness, achieving precise fire oppugning and supporting quick reaction maneuver. The paper brings forward three modes of hierarchical support mode, full-dimensional support mode and direct support mode during air defense forces operation supported by space information.

Research on Individual Soldier Radio Surveillance Equipment for Battlefield Situation Awareness

Individual soldier plays a more and more important role in the complex environment of battlefield electromagnetism, and its combat ability should be improved rapidly. A situation awareness system based on individual soldier radio surveillance equipment is presented. The probable equipment adapts mini size and low power hardware and intelligent signal processing technology. In this system, every soldier acts as a reconnaissance sensor, and also he can be a node among the battlefield situation awareness network. The system can improve the ability of battlefield situation awareness and the combat ability in the future information battlefield.

Method for Constructing Three-Dimensional Display Model on Battlefield Situation Awareness

Using three-dimensional graphic technology to construct virtual battlefield, realize battlefield situation information visualization, enhance situation awareness fidelity and immersion, and improve battlefield situation awareness naturalness, is an important orientation of C4ISR (Command, Control, Communication, Computer, Intelligence, Surveillance and Reconnaissance) system. For normal three-dimensional model couldn't describe the dynamic changing of virtual scene, we put forward a method constructing and drawing flow of three-dimensional model based on the data model of application domain for large-scale virtual scenes. We validate the feasibility of the method combined with the radar power bubble on electronic jam model.