Multi-UAV cooperative maneuver decision-making for pursuitevasion using improved MADRL

Aiming at the problem of multi-UAV pursuit-evasion confrontation, a UAV cooperative maneuver method based on an improved multi-agent deep reinforcement learning(MADRL) is proposed. In this method, an improved Comm Net network based on a communication mechanism is introduced into a deep reinforcement learning algorithm to solve the multi-agent problem. A layer of gated recurrent unit(GRU) is added to the actor-network structure to remember historical environmental states. Subsequently,another GRU is designed as a communication channel in the Comm Net core network layer to refine communication information between UAVs. Finally, the simulation results of the algorithm in two sets of scenarios are given, and the results show that the method has good effectiveness and applicability.

Cooperative maneuver decision making for multi-UAV air combat based on incomplete information dynamic game

Cooperative autonomous air combat of multiple unmanned aerial vehicles(UAVs)is one of the main combat modes in future air warfare,which becomes even more complicated with highly changeable situation and uncertain information of the opponents.As such,this paper presents a cooperative decision-making method based on incomplete information dynamic game to generate maneuver strategies for multiple UAVs in air combat.Firstly,a cooperative situation assessment model is presented to measure the overall combat situation.Secondly,an incomplete information dynamic game model is proposed to model the dynamic process of air combat,and a dynamic Bayesian network is designed to infer the tactical intention of the opponent.Then a reinforcement learning framework based on multiagent deep deterministic policy gradient is established to obtain the perfect Bayes-Nash equilibrium solution of the air combat game model.Finally,a series of simulations are conducted to verify the effectiveness of the proposed method,and the simulation results show effective synergies and cooperative tactics.

Optimal maneuver strategy to improve the observability of angles-only rendezvous

This paper proposes an optimal maneuver strategy to improve the observability of angles-only rendezvous from the perspective of relative navigation.A set of dimensionless relative orbital elements(ROEs)is used to parameterize the relative motion,and the objective function of the observability of anglesonly navigation is established.An analytical solution of the optimal maneuver strategy to improve the observability of anglesonly navigation is obtained by means of numerical analysis.A set of dedicated semi-physical simulation system is built to test the performances of the proposed optimal maneuver strategy.Finally,the effectiveness of the method proposed in this paper is verified through the comparative analysis of the objective function of the observability of angles-only navigation and the performances of the angles-only navigation filter under different maneuver schemes.Compared with the cases without orbital maneuver,it is concluded that the tangential filtering accuracy with the optimal orbital maneuver at the terminal time is increased by 35%on average,and the radial and normal filtering accuracy is increased by 30%on average.

A two-step method to detect broadcast ephemeris unavailable periods caused by BeiDou satellite orbital maneuvers

The space constellation of the BeiDou navigation satellite system(BDS) is a hybrid constellation containing medium earth orbit(MEO) satellites, geostationary earth orbit(GEO) satellites, and inclined geosynchronous orbit(IGSO) satellites. Due to the geosynchronous characteristics of GEO and IGSO, GEO satellites and IGSO satellites often need to perform orbital maneuvers, which can affect the signal-inspace(SIS) availability performance of BeiDou satellites. A two-step detection method for BeiDou satellite orbital maneuvers has been proposed in this paper. The first step is to identify orbital maneuvers based on time series analysis of broadcast ephemeris, and the second step is to verify orbital maneuvers based on bidirectional orbit prediction. The two-step detection method was used to detect the orbital maneuvers of BeiDou satellites in 2019. Through the double guarantees of identification and verification,the detection accuracy of BeiDou satellite orbital maneuvers has been effectively improved. And the orbital maneuver detection results are continued to be used to assess the SIS availability of BeiDou satellites. The results show that the availability loss of GEO satellite orbital maneuvers is about 0.45%-1.07%, and the availability loss of IGSO satellite orbital maneuvers is about 0.12%-0.19%.

Optimal maneuvering strategy of spacecraft evasion based on angles-only measurement and observability analysis

Spacecraft orbit evasion is an effective method to ensure space safety. In the spacecraft’s orbital plane, the space non-cooperate target with autonomous approaching to the spacecraft may have a dangerous rendezvous. To deal with this problem, an optimal maneuvering strategy based on the relative navigation observability degree is proposed with angles-only measurements. A maneuver evasion relative navigation model in the spacecraft’s orbital plane is constructed and the observability measurement criteria with process noise and measurement noise are defined based on the posterior Cramer-Rao lower bound. Further, the optimal maneuver evasion strategy in spacecraft’s orbital plane based on the observability is proposed. The strategy provides a new idea for spacecraft to evade safety threats autonomously. Compared with the spacecraft evasion problem based on the absolute navigation, more accurate evasion results can be obtained. The simulation indicates that this optimal strategy can weaken the system’s observability and reduce the state estimation accuracy of the non-cooperative target, making it impossible for the non-cooperative target to accurately approach the spacecraft.

Air combat target maneuver trajectory prediction based on robust regularized Volterra series and adaptive ensemble online transfer learning

Target maneuver trajectory prediction is an important prerequisite for air combat situation awareness and maneuver decision-making.However,how to use a large amount of trajectory data generated by air combat confrontation training to achieve real-time and accurate prediction of target maneuver trajectory is an urgent problem to be solved.To solve this problem,in this paper,a hybrid algorithm based on transfer learning,online learning,ensemble learning,regularization technology,target maneuvering segmentation point recognition algorithm,and Volterra series,abbreviated as AERTrOS-Volterra is proposed.Firstly,the model makes full use of a large number of trajectory sample data generated by air combat confrontation training,and constructs a Tr-Volterra algorithm framework suitable for air combat target maneuver trajectory prediction,which realizes the extraction of effective information from the historical trajectory data.Secondly,in order to improve the real-time online prediction accuracy and robustness of the prediction model in complex electromagnetic environments,on the basis of the TrVolterra algorithm framework,a robust regularized online Sequential Volterra prediction model is proposed by integrating online learning method,regularization technology and inverse weighting calculation method based on the priori error.Finally,inspired by the preferable performance of models ensemble,ensemble learning scheme is also incorporated into our proposed algorithm,which adaptively updates the ensemble prediction model according to the performance of the model on real-time samples and the recognition results of target maneuvering segmentation points,including the adaptation of model weights;adaptation of parameters;and dynamic inclusion and removal of models.Compared with many existing time series prediction methods,the newly proposed target maneuver trajectory prediction algorithm can fully mine the prior knowledge contained in the historical data to assist the current prediction.The rationality and effectiveness of the proposed algorithm are verified by simulation on three sets of chaotic time series data sets and a set of real target maneuver trajectory data sets.

Impact point prediction guidance of ballistic missile in high maneuver penetration condition

An impact point prediction(IPP) guidance based on supervised learning is proposed to address the problem of precise guidance for the ballistic missile in high maneuver penetration condition.An accurate ballistic trajectory model is applied to generate training samples,and ablation experiments are conducted to determine the mapping relationship between the flight state and the impact point.At the same time,the impact point coordinates are decoupled to improve the prediction accuracy,and the sigmoid activation function is improved to ameliorate the prediction efficiency.Therefore,an IPP neural network model,which solves the contradiction between the accuracy and the speed of the IPP,is established.In view of the performance deviation of the divert control system,the mapping relationship between the guidance parameters and the impact deviation is analysed based on the variational principle.In addition,a fast iterative model of guidance parameters is designed for reference to the Newton iteration method,which solves the nonlinear strong coupling problem of the guidance parameter solution.Monte Carlo simulation results show that the prediction accuracy of the impact point is high,with a 3 σ prediction error of 4.5 m,and the guidance method is robust,with a 3 σ error of 7.5 m.On the STM32F407 singlechip microcomputer,a single IPP takes about 2.374 ms,and a single guidance solution takes about9.936 ms,which has a good real-time performance and a certain engineering application value.

Incidence,characteristics and risk factors for alveolar recruitment maneuver-related hypotension in patients undergoing laparoscopic colorectal cancer resection

BACKGROUND Alveolar recruitment maneuvers(ARMs)may lead to transient hypotension,but the clinical characteristics of this induced hypotension are poorly understood.We investigated the characteristics of ARM-related hypotension in patients who underwent laparoscopic colorectal cancer resection.AIM To investigate the characteristics of ARM-related hypotension in patients who underwent laparoscopic colorectal cancer resection.METHODS This was a secondary analysis of the PROtective Ventilation using Open Lung approach Or Not trial and included 140 subjects.An ARM was repeated every 30 min during intraoperative mechanical ventilation.The primary endpoint was ARM-related hypotension,defined as a mean arterial pressure(MAP)<60 mmHg during an ARM or within 5 min after an ARM.The risk factors for hypotension were identified.The peri-ARM changes in blood pressure were analyzed for the first three ARMs(ARM_(1,2,3))and the last ARM(ARMl_(ast)).RESULTS Thirty-four subjects(24.3%)developed ARM-related hypotension.Of all 1027 ARMs,37(3.61%)induced hypotension.More ARMs under nonpneumoperitoneum(33/349,9.46%)than under pneumoperitoneum conditions(4/678,0.59%)induced hypotension(P<0.01).The incidence of hypotension was higher at ARM_(1)points than at non-ARM_(1)points(18/135,13.3%vs 19/892,2.1%;P<0.01).The median percentage decrease in the MAP at ARM1 was 14%.Age≥74 years,blood loss≥150 mL and peak inspiratory pressure under pneumoperitoneum<24 cm H_(2)O were risk factors for ARM-related hypotension.CONCLUSION When the ARM was repeated intraoperatively,a quarter of subjects developed ARM-related hypotension,but only 3.61%of ARMs induced hypotension.ARM-related hypotension most occurred in a hemodynamically unstable state or a hypovolemic state,and in elderly subjects.Fortunately,ARMs that were performed under pneumoperitoneum conditions had less impact on blood pressure.

Situational continuity-based air combat autonomous maneuvering decision-making

In order to improve the performance of UAV's autonomous maneuvering decision-making,this paper proposes a decision-making method based on situational continuity.The algorithm in this paper designs a situation evaluation function with strong guidance,then trains the Long Short-Term Memory(LSTM)under the framework of Deep Q Network(DQN)for air combat maneuvering decision-making.Considering the continuity between adjacent situations,the method takes multiple consecutive situations as one input of the neural network.To reflect the difference between adjacent situations,the method takes the difference of situation evaluation value as the reward of reinforcement learning.In different scenarios,the algorithm proposed in this paper is compared with the algorithm based on the Fully Neural Network(FNN)and the algorithm based on statistical principles respectively.The results show that,compared with the FNN algorithm,the algorithm proposed in this paper is more accurate and forwardlooking.Compared with the algorithm based on the statistical principles,the decision-making of the algorithm proposed in this paper is more efficient and its real-time performance is better.

潜艇强机动变深规避鱼雷攻击仿真研究

潜艇利用强机动变深规避鱼雷末端攻击弹道是其规避鱼雷攻击的方法之一,也是潜艇防御鱼雷的最后措施。研究这种规避方法的可行性和有效性,可以为潜艇指挥员科学合理地操纵潜艇规避鱼雷攻击提供参考,对提高潜艇生命力和战斗力具有十分重要的理论研究意义和军事应用价值。通过对强机动规避方案基本原理及不同机动方式的效果进行理论剖析,并对潜艇和鱼雷相对运动态势进行建模仿真计算,验证了潜艇强机动变深规避鱼雷攻击的效果。仿真计算表明,在一定距离范围内,高速机动的潜艇利用高压气吹除主压载水舱的水,同时采用上浮大舵角使潜艇大尾倾高速上浮至水面来规避鱼雷末端攻击弹道,随后迅速下潜到浅水层完成规避鱼雷攻击,这种战术机动规避措施在理论上是可行的,但是该方案采用时机亦应当充分考虑分析。

基于Transformer复杂运动辨识的机动星凸形扩展目标跟踪方法

针对复杂的机动扩展目标跟踪问题,利用Transformer网络设计了一种有效的星凸不规则形状机动扩展目标跟踪方法。首先,该文研究利用alpha-shape算法建立了星凸形状的变化模型,实现了静态场景下的星凸形扩展目标的形状估计。然后,通过对目标状态转移矩阵进行重新设计,结合Transformer网络对机动扩展目标运动状态转移矩阵进行实时估计,实现了对复杂机动目标运动过程的精准跟踪。进一步地,将估计得到的形状轮廓与运动状态进行融合,最终实现了对星凸形机动扩展目标的实时跟踪。最后,通过构造复杂的机动扩展目标跟踪场景,利用多重性能指标测试算法对形状和运动状态的综合估计性能,验证了算法的有效性。

肺复张在急性A型主动脉夹层术后低氧血症中的应用

目的 探讨肺复张治疗急性A型主动脉夹层(acute type A aortic dissection,ATAAD)术后低氧血症的疗效及安全性。方法 选取2019年11月至2022年5月广西医科大学第一附属医院ATAAD术后低氧血症患者56例,将其随机分为肺复张组(n=36)及常规治疗组(n=20)。常规治疗组患者在肺保护性通气基础上行常规机械通气,肺复张组患者采用呼气末正压通气(positive end expiratory pressure,PEEP)递增法进行肺复张。比较两组患者治疗前后的动脉血气分析、呼吸力学指标、血流动力学指标和血清白细胞介素(interleukin,IL)-6及IL-10水平。结果 治疗后12h、24h,两组患者的动脉血氧分压(arterial partial pressure of oxygen,PaO_(2))、氧合指数(oxygenation index,OI)、肺静态顺应性(C_(stat))及肺动态顺应性(C_(dyn))均显著高于本组治疗前,肺泡动脉氧分压差[PO_(2)(A-a)]、呼吸指数(respiratory index,RI)、气道峰压(P_(peak))及气道平台压(P_(plat))均显著低于本组治疗前(P<0.05);肺复张组患者的PaO_(2)、OI、C_(stat)及C_(dyn)均显著高于常规治疗组,PO_(2)(A-a)、RI、P_(peak)及P_(plat)均显著低于常规治疗组(P<0.05)。肺复张组患者肺复张过程中收缩压及平均动脉压有所下降(P<0.05),中心静脉压有所升高(P<0.05),肺复张结束后均恢复至基线水平。治疗后12h,两组患者的血清IL-6和IL-10水平均显著低于本组治疗前(P<0.05)。结论 PEEP递增法肺复张可改善ATAAD术后低氧血症患者的氧合及肺顺应性,但对血流动力学存在一过性影响,治疗时应进行严密监测。

矢量泵喷推进潜航器回转性能研究

泵喷推进器已经逐渐成为现代潜航器的首选,矢量推进器也在航空航天领域得到了广泛的应用。为了解决传统依赖船舵控制在低航速下控制效率低的问题以及提高潜航器回转性能,矢量推进器在潜航器上的应用已逐步成为国内外关注的热点。本文考虑大舵角(喷管偏角)下潜航器各参数非线性的影响,建立潜航器水平面操纵运动的非线性模型。通过仿真研究分析船-舵-推进器、船-矢量推进器和船-舵-矢量推进器三种不同的控制方法下潜航器在低航速、控制泵喷推进器转速、控制潜航器轴向航速三种不同条件下作水平面回转运动时的回转性能,结果表明矢量泵喷推进器可以有效改进潜航器的回转性能。

基于多特征融合的高机动多目标低截获概率跟踪技术

在多目标跟踪过程中,目标的高机动特性使得传统采用固定运动模型或交互式多模型的目标跟踪算法很难实时精确匹配目标运动模型,从而引起高机动目标的低跟踪精度问题。针对这一问题,本文提出一种基于目标运动状态模型自适应更新的高机动多目标跟踪算法。在多目标跟踪过程中,该算法采用多特征聚类融合算法进行目标运动模型估计,并根据各目标跟踪波动参数进行状态转移矩阵决策更新,同时利用联合概率数据关联实现多机动目标状态转移矩阵自适应更新的关联跟踪,从而解决了传统多目标跟踪算法因目标运动模型失配引起的低跟踪精度问题。在目标跟踪算法的传感器选择上,无源传感器不对外辐射能量,具有较好的低截获概率性能,但其跟踪精度有限,常不能满足多目标高跟踪精度的要求。雷达作为有源传感器,具有较高的跟踪精度。但由于雷达对外辐射信号,容易被防御方截获。针对这一问题,本文提出了一种无源传感器目标跟踪为主,有源雷达间歇跟踪为辅的多传感器协同管理目标跟踪算法。该算法通过对目标跟踪本征堆积误差的判断进行传感器的最优分配,并根据波动参数的大小进行状态转移矩阵决策更新。仿真结果验证了本文所提出的多传感器协同的高机动目标跟踪算法在满足高机动目标跟踪精度的条件下可以有效的提升雷达低截获概率性能。

内热针联合康复手法治疗创伤性膝关节僵直的观察

目的探讨内热针联合Maitland手法对创伤性膝关节僵直的治疗效果。方法收集2022年5月至2023年3月就诊于宁夏医科大学总医院康复科、骨科门诊及在院康复患者68例,采用随机数表法分成观察组(n=34例)和对照组(n=34例),比较两组患膝疼痛程度、运动功能及炎性因子的变化。结果两组一般资料比较差异均无统计学意义(P均>0.05)。两组治疗后美国特种外科医院膝关节(HSS)评分、膝关节活动度(ROM)评分较前提高,视觉模拟评分(VAS)和炎性因子水平均下降(P均<0.05),且观察组均优于对照组(P均<0.05)。结论内热针联合Maitland手法可有效改善PTKS的膝关节功能和疼痛,同时,炎性因子水平会随着疗效的改善而下降。

基于自抗扰方法的无人机控制律设计

针对多平台、多任务的无人机飞行控制律的快速开发问题,提出一种基于自抗扰方法的无人机控制律架构,设计了可复用的扩展状态观测器及微分跟踪器,研究对比了其在3种具有较大差异的无人机平台中的应用,使7 000 kg的超音速UAV_A获得了更优的敏捷性结果,在60 kg的缩比UAV_B完成了5.8g的半滚倒转大过载机动飞行试验,基于10 kg的平直翼UAV_C实现了12架机紧编队的精确轨迹控制飞行试验。仿真及试飞结果表明:所提自抗扰控制结构响应快速、控制精度高、鲁棒性强,能够较好地适应多类无人机、面向多种任务场景的控制需求,且不需调参就能够获得较好的控制效果,为飞行控制算法设计提供了新的技术途径。

非线性量测下的机动多目标跟踪

为了解决非线性量测下机动多目标跟踪实时性差、跟踪误差大以及对杂波变化鲁棒性较差的问题,基于随机有限集理论,提出了一种采用量测转换和模糊算法改进的多模型δ-广义标签多伯努利滤波器。首先,推导了交互多模型的δ-GLMB滤波器,通过去相关无偏量测转换实现位置量测从极坐标系到笛卡尔坐标系的无偏转换,并通过预测值去除量测误差和其协方差的相关性造成的滤波估计偏差,实现了非线性场景下的机动多目标跟踪;然后,通过航迹和量测的关联新息以及目标的机动约束构建联合波门,降低了杂波量测的数量;最后引入改进的模糊算法,以目标的模型后验概率为输入,根据模型的分离程度自适应调节运动模型的过程噪声,增加滤波精度。研究结果表明:在杂波环境下,通过与CKF-JMS-δ-GLMB、CKF-IMM-δ-GLMB等非线性多模型滤波器对比,所提算法计算时间较小,且跟踪精度更高,鲁棒性强。所提算法避免了传统的非线性处理方式计算量较大的问题,并且具有较好的杂波抑制特性,提升了非线性量测下机动多目标跟踪的性能。

美国定向能机动近程防空计划进展分析

美国定向能机动近程防空(directed energy maneuver-short range air defense,DE M-SHORAD)计划通过击伤、摧毁或压制旋转翼无人机、固定翼无人机以及火箭弹、火炮炮弹、迫击炮弹(rockets,artillery and mortar,RAM)等威胁目标,为机动部队提供伴随防空,对抗新兴威胁,属于美国陆军防空反导现代化的优先项目之一。首先介绍了DE M-SHORAD研制计划;其次详细分析了系统结构,并由系统参数评估了系统的作战性能;最后梳理了系统的研制进展。通过综合分析可知,DE M-SHORAD系统采用最佳组件,通过快速原型方法实现激光武器系统在装甲车上的集成;为降低技术风险,该计划在发展方式上分为两个阶段,首先集成、测试2 kW~5 kW机动实验型高能激光器(mobile experimental high-energy laser,MEHEL),然后再研制50 kW级的多任务高能激光器(multi-mission high-energy laser,MMHEL)。经计算可得:MEHEL和MMHEL对无人机的最大射程分别约为0.77 km、4.8 km。

基于局部变分贝叶斯推断的分布式交互式多模型估计

针对目前部分多模型算法预先设定运动模型转移概率矩阵对状态估计精度的不利影响,本文提出了一种基于局部变分贝叶斯推断的分布式交互式多模型估计算法.不同于传统交互式多模型估计中运动模型转移概率矩阵为先验已知的假设条件,在分布融合估计框架下,首先基于最小化Kullback-Leibler散度准则的递归优化策略实现对运动模型转移概率矩阵的预测与更新;在此基础上,结合变分贝叶斯推断实现对当前时刻目标状态与模型概率的联合估计;最后依据协方差交叉融合策略完成对局部状态估计融合.仿真结果表明:新算法通过对运动模型转移概率矩阵以及模型概率自适应在线估计,有效提升了机动目标的状态估计精度.

基于分层强化学习的低过载比拦截制导律

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