To solve the problem of offset failure of flexible single-link manipulator actuator due to actuator aging and wear,an adaptive boundary fault-tolerant control combining hysteresis quantizer and event trigger mechanism is proposed.Firstly,a hysteresis quantizer with unknown parameters is used to quantize the control input signal.Secondly,a static event trigger mechanism is constructed to reduce the consumption of input signal resources.Furthermore,the adaptive boundary fault-tolerant control law and parameter updation law are designed based on the improved Lyapunov direct method.Finally,the simulation results show that the proposed control algorithm is feasible.With communication constraints and disturbances,the proposed control method can achieve better posture tracking effect compared with traditional adaptive methods,while achieving system stability with lower computational load.
Radar direction finding is an essential cooperative monitoring method for aircraft,and the directional parameters of the direction finding antenna determine the accuracy of direction finding.A modified amplitude comparison direction finding model is proposed based on the traditional principle of adjacent amplitude comparison direction finding and the error of amplitude comparison direction finding.Based on the correction of amplitude comparison direction finding model,an error correction genetic simulated annealing(COR-GSA)algorithm is proposed to identify unknown directional antenna directionality parameters by determining the antenna directionality parameters that need to be identified.Identification experiments are conducted using a self-designed dual channel direction finding receiver,and 1 000 sets of aircraft real azimuth data are used for verification.The results show that using the COR-GSA algorithm to identify antenna directional parameters has the highest direction finding accuracy.Finally,the identified antenna directionality parameters are used to track the aircraft's azimuth,and the tracking error is reduced by 18.3％compared to the tracking error without azimuth correction.
With the expansion of user scale in LEO satellite networks, unbalanced regional load and bursty network traffic lead to the problem of load disequilibrium. A distributed hops-based back-pressure (DHBP) routing is proposed. DHBP theoretically derives a fast solution for the minimum end-to-end propagation hops between satellite nodes in inclined-orbit LEO satellite networks; hence, link weights are determined based on remaining hops between the next hop and destination satellites. In order to control the number of available retransmission paths, the permitted propagation region is restricted to a rectangular region consisting of source-destination nodes to reduce the propagation cost. Finally, DHBP is designed distributedly, to realize a dynamic selection of the shortest link with low congestion and balanced traffic distribution without obtaining the whole network topology. Network simulation results demonstrate that DHBP has higher throughput and lower delay under high load conditions compared with state-of-the-art routing protocols.
Compact high-frequency surface wave radar suffers from a high false alarm rate in target detection due to its low transmit power and wide beam, thus a large number of false plots are produced, which increases the computational burden of subsequent target tracking algorithm and easily leads to producing false tracks. In this paper, a two-stage false plot identification method is proposed. Firstly, a multi-frame plot clustering algorithm is proposed to cluster the potential plots of the same target in several consecutive frames, the plots outside the clusters are removed as false plots. Then, the differences in terms of range and Doppler velocity between the plot in the center frame and those in its neighbor frames in each cluster are used as features. Finally, a trained extreme learning machine is applied to the obtained features to recognize the remaining false plots. Experimental results with both simulated and field data demonstrate the effectiveness of the proposed method for false plot identification.
Sparse arrays are flexible and can reduce inter-array coupling while increasing the array aperture, but traditional DOA estimation based on sparse arrays can lead to angular ambiguity and confusion, which brings problems of poor estimation accuracy and insufficient robustness. In this paper, we propose a robust matrix filling algorithm with weighted truncated singular value projection (WT-SVP) for DOA estimation of sparse arrays, in which weights are assigned according to the size of singular values during the filling iteration to highlight the array information contained in large singular values and reduce the unnecessary weights in small singular values. The traditional singular value projection algorithm is optimized by reducing unnecessary noise information in small singular values. The algorithm can achieve hole information recovery of sparse arrays and make full use of discontinuous array elements, while the WT-SVP filling algorithm achieves high accuracy and high resolution of sparse array DOA estimation and high robustness at low signal-to-noise ratio and low snapshot.
Due to the existing matrix constant false alarm rate(CFAR)detectors only processing single polarized echoes and their high computational complexity,the use of correlation information is insufficient and the detection scenario is limited.In this regard,a detection method based on the maximum eigenvalue of dual polarization is proposed,which utilizes the covariance matrix of dual polarization radar echoes to measure the correlation between echoes by extracting their eigenvalues and has lower computational complexity.Firstly,the feasibility of using the maximum eigenvalue as the geometric distance for detection is derived.Furthermore,a matrix CFAR detector based on dual polarization maximum eigenvalue is designed for practical use in real-world scenarios.Finally,the actual detection capability of the proposed method is verified through measured sea clutter data.Simulation results show that the proposed method has better clutter suppression performance and detection performance compared to the maximum eigenvalue matrix measurement algorithm,which also uses the maximum eigenvalue as the detection statistic.The detection performance is also better than the non coherent accumulation cell-averaging CFAR detection method.
The widespread use of non-orthogonal multiple access (NOMA) has changed the traditional physical layer security limits on per-user transmission rates, with the attendant increase in complexity and system energy consumption. In view of this, a secure transmission method for mobile edge computing based on NOMA hybrid collaborative interference is proposed. Firstly, a multi-slot hybrid collaboration scheme is designed for each user data processing process for the task offloading problem. The scheme eliminates auxiliary interference signals transferred the offload task to local computing in the specified time slot. Then the scheme optimizes the total energy consumption of the system while ensuring fairness among users. Secondly, a closed expression for the system secrecy outage probability is derived. And the optimization problem is decomposed into three sub-problems. Finally, the block coordinate descent algorithm is used to iterate cyclically. With the objective of minimizing system energy consumption, the best offloading decision scheme for the user is obtained and compared with orthogonal multiple access and full offloading schemes. With reasonable time slots, the simulation results demonstrate the proposed solutions can well weigh the relationship between security and energy consumption. And it can effectively reduce system energy consumption while ensuring information security.
基于模型的系统工程(model-based systems engineering,MBSE)能够提升复杂工程的总体设计能力和设计效率,在工程领域得到了广泛应用,但在载人航天工程领域尚处于起步阶段.在载人航天任务方案论证与方案设计阶段,为了规范基于模型的需求分析与系统设计工作,提出了任务需求分析的初步工作方法和流程.基于MBSE方法论和载人航天工程特点,提出了开展任务需求分析、能力需求分析、系统架构设计、系统需求分析、仿真验证、需求发布6个步骤,并以美国阿尔特弥斯计划为案例,详述了需求分析的流程.为后续开发覆盖载人航天全任务周期的数字化设计和技术管理流程奠定了基础.
战斗机-无人机编组协同(fighter-drone teaming,FDT)是人员-信息-物理-智能一体化的全新作战形态,其系统需求捕获与验证方法尚没有工程先例和经验可循,亟需正向探索和实践.在一般的基于模型的系统工程(model-based systems engineering,MBSE)方法基础上,提出整体纳入跨装备协同要素、将分析模型和验证模型分开构建、同步考量逻辑行为与时空关系的捕获与验证策略,以及适用于编组协同"任务场景构建-系统行为分析-需求映射定义-模型在环验证"的流程和具体方法,进而以典型"二带二"对地FDT为例进行应用检验.检验结果表明,该方法可满足工程总体多专业联合工作需要,能够高效形成编组协同系统需求清单及相应的捕获与验证依据,从而为后续的系统设计和实现确立清晰的开发目标和设计指导.
海面雷达散射的空间遍历性是开展海面散射数值仿真以及分析不同体制雷达系统在海洋遥感应用中算法适用性的重要基础之一.针对随机粗糙海面雷达散射及其杂波幅度统计特性的空间遍历性问题,基于Monte Carlo方 法构建包含全部大尺度波浪的粗糙海面模型,利用高精度的全波数值(multilevel steepest decent-sparse matrix canonical grid,MLSD-SMCG)方法以及二阶小斜率近似(2nd-order small slope approximation,SSA-2)模型,分别对不同雷达照射尺寸的一维和二维粗糙海面L波段归一化雷达散射系数及海杂波进行仿真,并对海面雷达散射和海杂波幅度分布的空间遍历性进行分析.仿真结果表明:在无海面遮挡效应的雷达入射和散射角度范围内,雷达照射尺寸大于16λ(入射波波长)粗糙海面的同极化归一化雷达散射系数,具有良好的空间遍历性;当雷达照射海面尺寸不小于对应海况下最大风浪尺度的25％时,海面交叉极化的归一化雷达后向散射系数也表现出空间遍历性.相比海面散射的归一化雷达散射系数,单站海杂波幅度分布在高海况条件下空间遍历性减弱.同极化海杂波幅度分布随着入射角增大,表现出更显著的空间遍历性,而交叉极化情况则相反.
智能辅助驾驶应用场景对图像去雾的准确性和实时性要求较高.提出了一种新颖的基于感知融合机制的渐进式去雾网络(progressive dehaze network,PD-Net),将降质图像恢复的任务分解为多阶段的子任务,通过轻量级的子网分块学习特征图的不同区域语义信息,以提升去雾效率.在此基础上,基于注意力机制和导向滤波设计跨阶段感知融合模块(perception fusion module,PFM),自适应感知各阶段提取的多尺度特征并进行融合,而不损失图像细节信息及边缘结构信息.实验结果表明,与现有主流的端对端去雾模型相比,所提出的算法在处理户外图像时具有更高的准确度和实时性,在公开的合成对象测试集(synthetic object testing set,SOTS)上的峰值信噪比(peak signal to noise ratio,PSNR)与现有最好结果相比提升了 0.93 dB,处理单幅图像仅需72 ms,提出的网络模型有望应用于智能交通等现实领域.
Aiming at the time cooperative route planning problem of multiple unmanned aerial vehicles（UAVs）, an online three-dimensional planning method based on hierarchical decomposition is proposed. Firstly, the high dimensional strong coupling cooperative planning problem is decomposed into a low dimensional simple optimization problem according to 3 layers. Secondly, an optimization method of cooperative index parameters based on the inverse hyperbolic tangent function is presented in order to solve the diversion consumption problem caused by excessive time intervals between UAVs. Then, an online route planning algorithm of three-dimensional rapidly-exploring random tree * based on receding horizon（TRH-RRT * ） is proposed. The biased random samples are employed to increase the utilization of sampling points, the artificial potential field is exploited to guide the growth of RRT * nodes and the node removal method based on receding horizon is applied to reducing the unnecessary scanning process. Finally, the simulation results for attack mission of UAVs convergence show that the proposed method possesses advantages in planning time and planning results.
在遥感图像分类实际应用中,深度学习经常面临高光谱数据有效标签不完备、样本多类不平衡和数据分布随时空动态变化等问题,难以发挥优势.基于上述问题,提出一个基于人工少数类过采样方法(synthetic minority oversampling technique,SMOTE)和深度迁移卷积神经网络的土地覆盖分类算法.所提算法创新性地采用深度迁移学习,使算法能够学习不同时空相同地物的相似性,并利用SMOTE方法对学习数据进行类分布空间优化平衡,从而解决 目标域数据不足和数据类不平衡问题.两组公开的高光谱遥感图像被用来验证所提算法的有效性.实验结果表明,相比传统的深度学习,所提算法能够更有效地解决数据不足和数据类不平衡问题提高分类精度.
对合成孔径雷达(synthetic aperture radar,SAR)实施欺骗干扰,干扰方通常需要预先侦察出SAR载机平台速度、方位向慢时间、SAR载机平台到干扰机的最近斜距以及干扰机自身位置等关键参数,而获取这些参数通常需要复杂的侦察设备.针对这一问题,提出了 一种基于多接收机协同的无源测向及SAR欺骗干扰方法,利用干扰机和接收机的布站以及接收机间的到达时差(time difference of arrival,TDOA)信息,有效地解决了需复杂设备侦察上述关键参数的难题,简化了干扰系统的配置.根据TDOA信息,可计算出干扰机相对于SAR载机平台的俯仰角和水平方位角,从而实现对SAR载机平台的无源测向.在此基础上,提出了 一种对SAR实施欺骗干扰的算法,并与现有基于多接收机协同的SAR欺骗干扰方法进行了对比.对比结果表明,随着生成的虚假目标距离干扰机的距离增大,所提方法比现有方法的目标聚焦效果更好,且实时性更强.
快速上行授权接入是大规模机器类通信的关键技术之一,而缓解上行共享信道资源紧张并针对时延和速率等服务质量(quality of services,QoS)高效地进行调度是对其进行优化的重要方向.针对这一问题,提出一种应用多臂赌博机(multi-armed bandit,MAB)学习和功率域非 正交多址接入(power-domain non-orthogonal multiple access,PD-NOMA)技术的快速上行授权接入算法.所提算法通过多路MAB筛选高接入速率、低接入忍耐时延要求和低接入速率、低接入忍耐时延要求的两类设备,允许其优先被调度并复用上行资源进行接入.仿真结果表明,算法降低了系统的上行资源浪费率,在提高了接入能力的同时减少了因非正交多址接入(non-or-thogonal multiple access,NOMA)造成速率损失带来的影响,并优化了系统QoS.
At present， the UAV type recognition algorithms in the literature only realize the identification of a single UAV type through the signal characteristics of the communication domain or radar domain， and there are problems such as low recognition accuracy. Aiming at the above problems， this paper proposes a UAV swarm type recognition algorithm based on the fusion characteristics of communication signals and radar signals. First， the high-order cumulant and instantaneous feature statistics of the swarm communication signal are extracted， and the radar track features are fused to construct the UAV swarm feature matrix; secondly， an improved feature selection algorithm—Secondary Screening of Neighbourhood Components Analysis （SSNCA） is proposed to reduce the dimensionality of the fusion feature matrix; finally， a Sparse Autoencoder Network is used for swarm type identification. The simulation results show that the algorithm significantly reduces the dimension of the swarm feature matrix （only 27% of the original matrix dimension）; at the same time， when the signal-to-noise ratio is 0 dB， the correct rate of identifying five swarm types can reach 88%.
随着现代电子战技术的发展,机载雷达面临的战场环境日趋复杂.传统机载雷达往往发射固定的波形,很难在复杂多变的电磁环境和动态时变杂波的环境下有效完成 目标的检测和跟踪任务.以认知雷达和多输入多输出(multiple-input multiple-output,MIMO)雷达为代表的新体制雷达,通过发射端灵活设计与环境相匹配的波形,提升了机载雷达在复杂战场环境下的适应能力.对新体制机载雷达波形优化设计的研究与发展进行了综述.首先,系统阐述了认知雷达的基本原理,并概述了新体制机载雷达波形优化设计;然后,分别从先验条件和收发处理的角度对新体制机载雷达波形优化设计的研究成果进行了梳理;最后,针对当前机载雷达波形优化技术存在的问题,对未来新体制机载雷达波形优化设计的发展趋势进行了展望.
全球导航卫星系统(global navigation satellite system,GNSS)有诸多优势,但在一些有物理阻隔或电磁干扰的领域,GNSS的可用性、可靠性、精确性出现明显不足,由此可以使用一种基于地面设备的伪卫星系统进行导航定位服务.伪卫星信号体制细节公开,使得接收机易受到多种欺骗干扰的攻击.为了使信号传输更加安全,提出一种使用导航电文加密方法的信号防伪认证方案,并从安全性、可靠性和认证效率几方面对该方案进行评估.分析表明,所提方案能在保证较好的认证实效的同时,维持较低的通信成本与计算成本,解决了伪卫星系统缺乏认证机制、存在安全隐患的问题.
针对目前强化试验剖面效率低、成本高的问题,提出了某型弹类电子产品温度强化试验剖面设计框架.结合产品可靠性框图对试验对象进行失效逻辑分析,基于元器件降额的步长设计方法(step design method based on component derating,CD-SDM)优化步长、缩短试验时间,采用基于有限元仿真的确定性分析方法获得工作极限和破坏极限估值,降低步长划分时极限间工作裕度的影响,实现步长、试验时间和其他要素的优化.以某型弹类电子产品高温步进为例验证所提方法,结果表明获得的温度强化试验剖面较传统方法在试验时间上最少可缩短13.33％左右,与传统方法相比减少了 1/4的检测次数,优化了 目前可靠性强化试验剖面设计对弹类电子产品试验效率低、成本高的问题.
We propose a probabilistic neural network multi-epoch residual receiver autonomous integrity monitoring （PMR-RAIM） algorithm for civil aviation which can improve the detection capability of RAIM algorithm for fault deviation and reduce the minimum detectable deviation. A four-layer fault satellite detection model based on probabilistic neural network is constructed. Fault class and fault-free class training samples of pseudorange residuals are established using variance inflation model. The smoothing parameter of probabilistic neural network are optimized by particle swarm optimization algorithm to meet the false alarm rate. Thus， the similarity between the input multi-epoch pseudorange residual and the fault samples and fault-free samples can be calculated to determine whether the satellite is faulty. Simulation results suggest that optimizing the smoothing parameter can improve the fault detection ability of the proposed algorithm. Compared with the weighted least squares RAIM algorithm and the ARAIM algorithm， the proposed algorithm can improve the detection performance of small pseudorange deviation and reduce the minimum detectable deviation under different fault conditions.
