Maximizing the probability an aerial anti-submarine torpedo detects its target

As a result of the high speed of anti-submarine patrol aircraft as well as their wide range, high efficiency and other characteristics, aerial torpedoes released by anti-submarine patrol aircraft have become the key anti submarine tool. In order to improve operational efficiency, a deep study was made of the target detection probabilities for aerial torpedoes released by anti-submarine patrol aircraft. The operational modes of aerial torpedoes were analyzed and mathematical-simulation models were then established. The detection probabilities of three attacking modes were then calculated. Measures were developed for improving low probabilities of detection when attacking a probable target position. This study provides an important frame of reference for the operation of aerial torpedo released by anti-submarine patrol aircraft.

Mastering air combat game with deep reinforcement learning

Reinforcement learning has been applied to air combat problems in recent years,and the idea of curriculum learning is often used for reinforcement learning,but traditional curriculum learning suffers from the problem of plasticity loss in neural networks.Plasticity loss is the difficulty of learning new knowledge after the network has converged.To this end,we propose a motivational curriculum learning distributed proximal policy optimization(MCLDPPO)algorithm,through which trained agents can significantly outperform the predictive game tree and mainstream reinforcement learning methods.The motivational curriculum learning is designed to help the agent gradually improve its combat ability by observing the agent's unsatisfactory performance and providing appropriate rewards as a guide.Furthermore,a complete tactical maneuver is encapsulated based on the existing air combat knowledge,and through the flexible use of these maneuvers,some tactics beyond human knowledge can be realized.In addition,we designed an interruption mechanism for the agent to increase the frequency of decisionmaking when the agent faces an emergency.When the number of threats received by the agent changes,the current action is interrupted in order to reacquire observations and make decisions again.Using the interruption mechanism can significantly improve the performance of the agent.To simulate actual air combat better,we use digital twin technology to simulate real air battles and propose a parallel battlefield mechanism that can run multiple simulation environments simultaneously,effectively improving data throughput.The experimental results demonstrate that the agent can fully utilize the situational information to make reasonable decisions and provide tactical adaptation in the air combat,verifying the effectiveness of the algorithmic framework proposed in this paper.

A function-based behavioral modeling method for air combat simulation

Today’s air combat has reached a high level of uncertainty where continuous or discrete variables with crisp values cannot be properly represented using fuzzy sets. With a set of membership functions, fuzzy logic is well-suited to tackle such complex states and actions. However, it is not necessary to fuzzify the variables that have definite discrete semantics.Hence, the aim of this study is to improve the level of model abstraction by proposing multiple levels of cascaded hierarchical structures from the perspective of function, namely, the functional decision tree. This method is developed to represent behavioral modeling of air combat systems, and its metamodel,execution mechanism, and code generation can provide a sound basis for function-based behavioral modeling. As a proof of concept, an air combat simulation is developed to validate this method and the results show that the fighter Alpha built using the proposed framework provides better performance than that using default scripts.

Optimal confrontation position selecting games model and its application to one-on-one air combat

In the air combat process,confrontation position is the critical factor to determine the confrontation situation,attack effect and escape probability of UAVs.Therefore,selecting the optimal confrontation position becomes the primary goal of maneuver decision-making.By taking the position as the UAV’s maneuver strategy,this paper constructs the optimal confrontation position selecting games(OCPSGs)model.In the OCPSGs model,the payoff function of each UAV is defined by the difference between the comprehensive advantages of both sides,and the strategy space of each UAV at every step is defined by its accessible space determined by the maneuverability.Then we design the limit approximation of mixed strategy Nash equilibrium(LAMSNQ)algorithm,which provides a method to determine the optimal probability distribution of positions in the strategy space.In the simulation phase,we assume the motions on three directions are independent and the strategy space is a cuboid to simplify the model.Several simulations are performed to verify the feasibility,effectiveness and stability of the algorithm.

Combating Cholera

Lying in her makeshift hospital bed,Joyce Tembo thanked medical personnel for evacuating her to the designated national cholera treatment centre,6 km north of Zambia’s capital Lusaka.She was recently diagnosed with diarrhoeal disease.Tembo,43,commended the medical sta!stationed at the treatment centre for their great service to thousands of patients,especially women and children seeking urgent treatment.“I am very grateful to the Chinese doctors who attended to me as soon as the ambulance rushed me to the clinic where I received urgent treatment;they have really saved my life,”Tembo told ChinAfrica.But not all residents in her community are as lucky as her.Many in the densely populated slums die every day due to the area’s poor sanitation-one of the major causes of the cholera outbreak.

HEURISTIC PARTICLE SWARM OPTIMIZATION ALGORITHM FOR AIR COMBAT DECISION-MAKING ON CMTA

Combining the heuristic algorithm （HA） developed based on the specific knowledge of the cooperative multiple target attack （CMTA） tactics and the particle swarm optimization （PSO）, a heuristic particle swarm optimization （HPSO） algorithm is proposed to solve the decision-making （DM） problem. HA facilitates to search the local optimum in the neighborhood of a solution, while the PSO algorithm tends to explore the search space for possible solutions. Combining the advantages of HA and PSO, HPSO algorithms can find out the global optimum quickly and efficiently. It obtains the DM solution by seeking for the optimal assignment of missiles of friendly fighter aircrafts （FAs） to hostile FAs. Simulation results show that the proposed algorithm is superior to the general PSO algorithm and two GA based algorithms in searching for the best solution to the DM problem.

ASSESSMENT OF AIRCRAFT COMBAT SURVIVABILITY ENHANCED BY COMBINED RADAR STEALTH AND ONBOARD ELECTRONIC ATTACK

The combat survivability is an essential factor to be considered in the development of recent military aircraft. Radar stealth and onboard electronic attack are two major techniques for the reduction of aircraft susceptibility. A tactical scenario for a strike mission is presented. The effect of aircraft radar cross section on the detection probability of a threat radar, as well as that of onboard jammer, are investigated. The guidance errors of radar guided surface to air missile and anti aircraft artillery, which are disturbed by radar cross section reduction or jammer radiated power and both of them are determined. The probability of aircraft kill given a single shot is calculated and finally the sortie survivability of an attack aircraft in a supposed hostile thread environment worked out. It is demonstrated that the survivability of a combat aircraft will be greatly enhanced by the combined radar stealth and onboard electronic attack, and the evaluation metho dology is effective and applicable.

OPTIMIZATION FOR COMBAT CONFIGURATION OF AIR DEFENSE WEAPON SYSTEMS

At evaluating the combat effectiveness of the defense system, target′s probability to penetrate the defended area is a primary care taking index. In this paper, stochastic model to compete the probability that target penetrates the defended area along any flight path is established by the state analysis and statistical equilibrium analysis of stochastic service system theory. The simulated annealing algorithm is an enlightening random search method based on Monte Carlo recursion, and it can find global optimal solution by simulating annealing process. Combining stochastic model to compete the probability and simulated annealing algorithm, this paper establishes the method to solve problem quantitatively about combat configuration optimization of weapon systems. The calculated result shows that the perfect configuration for fire cells of the weapon is fast found by using this method, and this quantificational method for combat configuration is faster and more scientific than previous one based on principle via map fire field.

“战斗力”能译成“combativeness”吗?

新华网英文版在一篇报道中把军事术语"战斗力"译成了"combativeness"。通过分析十四部英汉词典中"combativeness"的释义、八部汉英词典给出的"战斗力"的译法以及基于问卷调查的结果,作者指出了新华网这一译法的错误,补充了汉英词典未收录的"战斗力"的两种译法,并强调加强外宣翻译中政治意识的重要性。

Situation assessment for air combat based on novel semi-supervised naive Bayes

A method is proposed to resolve the typical problem of air combat situation assessment. Taking the one-to-one air combat as an example and on the basis of air combat data recorded by the air combat maneuvering instrument, the problem of air combat situation assessment is equivalent to the situation classification problem of air combat data. The fuzzy C-means clustering algorithm is proposed to cluster the selected air combat sample data and the situation classification of the data is determined by the data correlation analysis in combination with the clustering results and the pilots＇ description of the air combat process. On the basis of semi-supervised naive Bayes classifier, an improved algorithm is proposed based on data classification confidence, through which the situation classification of air combat data is carried out. The simulation results show that the improved algorithm can assess the air combat situation effectively and the improvement of the algorithm can promote the classification performance without significantly affecting the efficiency of the classifier.

Chinese expert consensus on the treatment of modern combat-related spinal injuries

The battlefield treatments of spinal and spinal cord injury vary from civilian settings. However, there is no unified battlefield treatment guidelines for spine trauma in PLA. An expert consensus is reached, based on spine trauma epidemiology and the concepts of battlefield treatment combined with the existing levels of military medical care in modern warfare. Since the specialized treatment for spine trauma are no significant difference between civilian settings and modern war, the first aid, emergency treatment and early treatment of spine trauma are introduced separately in three levels in this consensus. In Level I facilities, the fast and accurate evaluation of spine trauma followed by fixation and stabilization are recommended during the first-aid stage. Re-evaluation, further treatment for possible hemorrhagic shock, dyspnea and infection are recommended at Level II facilities. At Level III facilities, it is recommended to strengthen the intensive care and the prevention of urinary system and lung infection for the wounded with severe spinal injury, however, spinal surgery is not recommended in a battlefield hospital. The grading standard for evidence evaluation and recommendation was used to reach this expert consensus.

Expert consensus on the evaluation and diagnosis of combat injuries of the Chinese People's Liberation Army

The accurate assessment and diagnosis of combat injuries are the basis for triage and treatment of combat casualties. A consensus on the assessment and diagnosis of combat injuries was made and discussed at the second annual meeting of the Professional Committee on Disaster Medicine of the Chinese People's Liberation Army(PLA). In this consensus agreement, the massive hemorrhage, airway, respiration, circulation and hypothermia(MARCH) algorithm, which is a simple triage and rapid treatment and field triage score, was recommended to assess combat casualties during the first-aid stage, whereas the abbreviated scoring method for combat casualty and the MARCH algorithm were recommended to assess combat casualties in level Ⅱ facilities. In level Ⅲ facilities, combined measures, including a history inquiry, thorough physical examination, laboratory examination, X-ray, and ultrasound examination, were recommended for the diagnosis of combat casualties. In addition, corresponding methods were recommended for the recognition of casualties needing massive transfusions, assessment of firearm wounds, evaluation of mangled extremities, and assessment of injury severity in this consensus.

Autonomous air combat maneuver decision using Bayesian inference and moving horizon optimization

To reach a higher level of autonomy for unmanned combat aerial vehicle(UCAV) in air combat games, this paper builds an autonomous maneuver decision system. In this system,the air combat game is regarded as a Markov process, so that the air combat situation can be effectively calculated via Bayesian inference theory. According to the situation assessment result,adaptively adjusts the weights of maneuver decision factors, which makes the objective function more reasonable and ensures the superiority situation for UCAV. As the air combat game is characterized by highly dynamic and a significant amount of uncertainty,to enhance the robustness and effectiveness of maneuver decision results, fuzzy logic is used to build the functions of four maneuver decision factors. Accuracy prediction of opponent aircraft is also essential to ensure making a good decision; therefore, a prediction model of opponent aircraft is designed based on the elementary maneuver method. Finally, the moving horizon optimization strategy is used to effectively model the whole air combat maneuver decision process. Various simulations are performed on typical scenario test and close-in dogfight, the results sufficiently demonstrate the superiority of the designed maneuver decision method.

Medical rescue of naval combat: challenge and future

There has been no large-scale naval combat in the last 30 years. With the rapid development of battleships, weapons manufacturing and electronic technology, naval combat will present some new characteristics. Additionally, naval combat is facing unprecedented challenges. In this paper, we discuss the topic of medical rescue at sea: what challenges we face and what we could do. The contents discussed in this paper contain battlefield self-aid buddy care, clinical skills, organized health services, medical training and future medical research programs. We also discuss the characteristics of modern naval combat, medical rescue challenges, medical treatment highlights and future developments of medical rescue at sea.

Combat lifesaver-trained, first-responder application of junctional tourniquets: A prospective, randomized,crossover trial

Background:Junctional hemorrhage surpassed extremity hemorrhage as the leading cause of preventable death after the resurgence of limb tourniquets during the recent conflicts in Afghanistan and Iraq.Junctional tourniquets(JTQs)were developed in response to this injury pattern.Published data for JTQ efficacy are limited and do not incorporate nonmedical,military first responders.We compared the time for effective placement and scores for device satisfaction between two different JTQs,stratified by combat lifesaver(CLS)and combat medics.Methods:We performed a prospective,randomized,crossover trial utilizing the SAM ’ Medical Junctional Tourniquet(SJT)and Junctional Emergency Treatment Tool(JETTTM).Investigators simple randomized CLS and combat medics to SJT or JETT for their first JTQ application on mannequins with penetrating inguinal injuries.Then,participants immediately placed the other JTQ on another casualty with the same injury.The primary outcome measured was time of successful applicatio n.Success was defined as proper JTQ placement and a pressu re reading of at least 180 mmHg.We compared outcomes between CLS and combat medics.Unsuccessful JTQ applications were excluded from the comparative analysis.Results:From June 2015 to August 2015,a total of 227 personnel(133 CLS and 94 combat medics)at Fort Hood,Texas,USA volunteered to participate in the study.Twenty-eight percent(38 of 133)of CLS and 40%(38 of 94)of combat medics placed both JTQs successfully,for a total of 152 applications(76 SJTs and 76 JETTs).We found a significant difference between applications of the JETT between the CLS and combat medics(92.0±37.7 s versus70.5±20.5 s,P=0.004).No other subg roup analyses,whether by device or user,demonstrated a sig nifica nt difference in application time.Both groups preferred the SJT over the JETT.CLS disagreed with combat medics that the JETT could be easily applied by one person(median 3.0[2.0,4.0]versus median 4.0[3.0,5.0];P=0.006).Conclusions:Overall,success rates for both the SJT and JETT were low.Improved training is needed to increase successful application of junctional tourniquets before widespread implementation.Combat lifesavers and combat medics prefer the SJT over the JETT.

Extracting Decision Rules for Cooperative Team Air Combat Based on Rough Set Theory

In order to reduce redundant features in air combat information and to meet the requirements of real-time decision in combat, rough set theory is introduced to the tactical decision analysis in cooperative team air combat. An algorithm of attribute reduction for extracting key combat information and generating tactical rules from given air combat databases is presented. Then, considering the practical requirements of team combat, a method for reduction of attribute-values under single decision attribute is extended to the reduction under multi-decision attributes. Finally, the algorithm is verified with an example for tactical choices in team air combat. The results show that, the redundant attributes in air combat information can be reduced, and that the main combat attributes, i.e., the information about radar command and medium-range guided missile, can be obtained with the algorithm mentioned above, moreover, the minimal reduced strategy for tactical decision can be generated without losing the result of key information classification. The decision rules extracted agree with the real situation of team air combat.

Sequential maneuvering decisions based on multi-stage influence diagram in air combat

A multi-stage influence diagram is used to model the pilot＇s sequential decision making in one on one air combat. The model based on the multi-stage influence diagram graphically describes the elements of decision process, and contains a point-mass model for the dynamics of an aircraft and takes into account the decision maker＇s preferences under uncertain conditions. Considering an active opponent, the opponent＇s maneuvers can be modeled stochastically. The solution of multistage influence diagram can be obtained by converting the multistage influence diagram into a two-level optimization problem. The simulation results show the model is effective.

UAV cooperative air combat maneuver decision based on multi-agent reinforcement learning

In order to improve the autonomous ability of unmanned aerial vehicles(UAV)to implement air combat mission,many artificial intelligence-based autonomous air combat maneuver decision-making studies have been carried out,but these studies are often aimed at individual decision-making in 1 v1 scenarios which rarely happen in actual air combat.Based on the research of the 1 v1 autonomous air combat maneuver decision,this paper builds a multi-UAV cooperative air combat maneuver decision model based on multi-agent reinforcement learning.Firstly,a bidirectional recurrent neural network(BRNN)is used to achieve communication between UAV individuals,and the multi-UAV cooperative air combat maneuver decision model under the actor-critic architecture is established.Secondly,through combining with target allocation and air combat situation assessment,the tactical goal of the formation is merged with the reinforcement learning goal of every UAV,and a cooperative tactical maneuver policy is generated.The simulation results prove that the multi-UAV cooperative air combat maneuver decision model established in this paper can obtain the cooperative maneuver policy through reinforcement learning,the cooperative maneuver policy can guide UAVs to obtain the overall situational advantage and defeat the opponents under tactical cooperation.

Target distribution in cooperative combat based on Bayesian optimization algorithm

Target distribution in cooperative combat is a difficult and emphases. We build up the optimization model according to the rule of fire distribution. We have researched on the optimization model with BOA. The BOA can estimate the joint probability distribution of the variables with Bayesian network, and the new candidate solutions also can be generated by the joint distribution. The simulation example verified that the method could be used to solve the complex question, the operation was quickly and the solution was best.

Analysis on MAV/UAV cooperative combat based on complex network

A formation model of manned/unmanned aerial vehicle(MAV/UAV) collaborative combat can qualitatively and quantitatively analyze the synergistic effects.However,there is currently no effective and appropriate model construction method or theory,and research in the field of collaborative capability evaluation is basically nonexistent.According to the actual conditions of cooperative operations,a new MAV/UAV collaborative combat network model construction method based on a complex network is presented.By analyzing the characteristic parameters of the abstract network,the index system and complex network are combined.Then,a method for evaluating the synergistic effect of the cooperative combat network is developed.This method provides assistance for the verification and evaluation of MAV/UAV collaborative combat.