Distributed Resource Allocation in Dispersed Computing Environment Based on UAV Track Inspection in Urban Rail Transit

With the rapid development of urban rail transit,the existing track detection has some problems such as low efficiency and insufficient detection coverage,so an intelligent and automatic track detectionmethod based onUAV is urgently needed to avoid major safety accidents.At the same time,the geographical distribution of IoT devices results in the inefficient use of the significant computing potential held by a large number of devices.As a result,the Dispersed Computing(DCOMP)architecture enables collaborative computing between devices in the Internet of Everything(IoE),promotes low-latency and efficient cross-wide applications,and meets users’growing needs for computing performance and service quality.This paper focuses on examining the resource allocation challenge within a dispersed computing environment that utilizes UAV inspection tracks.Furthermore,the system takes into account both resource constraints and computational constraints and transforms the optimization problem into an energy minimization problem with computational constraints.The Markov Decision Process(MDP)model is employed to capture the connection between the dispersed computing resource allocation strategy and the system environment.Subsequently,a method based on Double Deep Q-Network(DDQN)is introduced to derive the optimal policy.Simultaneously,an experience replay mechanism is implemented to tackle the issue of increasing dimensionality.The experimental simulations validate the efficacy of the method across various scenarios.

Joint resource allocation scheme for target tracking in distributed MIMO radar systems

A joint resource allocation scheme concerned with the sensor subset,power and bandwidth for range-only target tracking in multiple-input multiple-output(MIMO)radar systems is proposed.By selecting an optimal subset of sensors with the predetermined size and implementing the power allocation and bandwidth strategies among them,this algorithm can help achieving a better performance within the same resource constraints.Firstly,the Bayesian Cramer-Rao bound(BCRB)is derived from it.Secondly,a criterion for minimizing the BCRB at the target location among all targets tracking in a certain range is derived.Thirdly,the optimization problem involved with three variable vectors is formulated,which can be simplified by deriving the relationship between the optimal power allocation vector and the bandwidth allocation vector.Then,the simplified optimization problem is solved by the cyclic minimization algorithm incorporated with the sequential parametric convex approximation(SPCA)algorithm.Finally,the validity of the proposed method is demonstrated with simulation results.

Joint target assignment and power allocation in the netted C-MIMO radar when tracking multi-targets in the presence of self-defense blanket jamming

The netted radar system(NRS)has been proved to possess unique advantages in anti-jamming and improving target tracking performance.Effective resource management can greatly ensure the combat capability of the NRS.In this paper,based on the netted collocated multiple input multiple output(CMIMO)radar,an effective joint target assignment and power allocation(JTAPA)strategy for tracking multi-targets under self-defense blanket jamming is proposed.An architecture based on the distributed fusion is used in the radar network to estimate target state parameters.By deriving the predicted conditional Cramer-Rao lower bound(PC-CRLB)based on the obtained state estimation information,the objective function is formulated.To maximize the worst case tracking accuracy,the proposed JTAPA strategy implements an online target assignment and power allocation of all active nodes,subject to some resource constraints.Since the formulated JTAPA is non-convex,we propose an efficient two-step solution strategy.In terms of the simulation results,the proposed algorithm can effectively improve tracking performance in the worst case.

Adaptive resource management for multi-target tracking in co-located MIMO radar based on time-space joint allocation

Compared with the traditional phased array radar, the co-located multiple-input multiple-output(MIMO) radar is able to transmit orthogonal waveforms to form different illuminating modes, providing a larger freedom degree in radar resource management. In order to implement the effective resource management for the co-located MIMO radar in multi-target tracking,this paper proposes a resource management optimization model,where the system resource consumption and the tracking accuracy requirements are considered comprehensively. An adaptive resource management algorithm for the co-located MIMO radar is obtained based on the proposed model, where the sub-array number, sampling period, transmitting energy, beam direction and working mode are adaptively controlled to realize the time-space resource joint allocation. Simulation results demonstrate the superiority of the proposed algorithm. Furthermore, the co-located MIMO radar using the proposed algorithm can satisfy the predetermined tracking accuracy requirements with less comprehensive cost compared with the phased array radar.

Control allocation for a class of morphing aircraft with integer constraints based on Lévy flight

Aiming at tracking control of a class of innovative control effector(ICE) aircraft with distributed arrays of actuators, this paper proposes a control allocation scheme based on the Lévy flight.Different from the conventional aircraft control allocation problem,the particular characteristic of actuators makes the actuator control command totally subject to integer constraints. In order to tackle this problem, first, the control allocation problem is described as an integer programming problem with two desired objectives. Then considering the requirement of real-time, a metaheuristic algorithm based on the Lévy flight is introduced to tackling this problem. In order to improve the searching efficiency, several targeted and heuristic strategies including variable step length and inherited population initialization according to feedback and so on are designed. Moreover, to prevent the incertitude of the metaheuristic algorithm and ensure the flight stability, a guaranteed control strategy is designed. Finally, a time-varying simulation model is introduced to verifying the effectiveness of the proposed scheme. The contrastive simulation results indicate that the proposed scheme achieves superior tracking performance with appropriate actuator dynamics and computational time, and the improvements for efficiency are active and the parameter settings are reasonable.

Control allocation for aircraft with input constraints based on improved cuckoo search algorithm

The control allocation problem of aircraft whose control inputs contain integer constraints is investigated. The control allocation problem is described as an integer programming problem and solved by the cuckoo search algorithm. In order to enhance the search capability of the cuckoo search algorithm, the adaptive detection probability and amplification factor are designed. Finally, the control allocation method based on the proposed improved cuckoo search algorithm is applied to the tracking control problem of the innovative control effector aircraft. The comparative simulation results demonstrate the superiority and effectiveness of the proposed improved cuckoo search algorithm in control allocation of aircraft.

Joint waveform selection and power allocation algorithm in manned/unmanned aerial vehicle hybrid swarm based on chance-constraint programming

In this paper, we propose a joint waveform selection and power allocation(JWSPA) strategy based on chance-constraint programming(CCP) for manned/unmanned aerial vehicle hybrid swarm(M/UAVHS) tracking a single target. Accordingly,the low probability of intercept(LPI) performance of system can be improved by collaboratively optimizing transmit power and waveform. For target radar cross section(RCS) prediction, we design a random RCS prediction model based on electromagnetic simulation(ES) of target. For waveform selection, we build a waveform library to adaptively manage the frequency modulation slope and pulse width of radar waveform. For power allocation,the CCP is employed to balance tracking accuracy and power resource. The Bayesian Cramér-Rao lower bound(BCRLB) is adopted as a criterion to measure target tracking accuracy. The hybrid intelli gent algorithms, in which the stochastic simulation is integrated into the genetic algorithm(GA), are used to solve the stochastic optimization problem. Simulation results demonstrate that the proposed JWSPA strategy can save more transmit power than the traditional fixed waveform scheme under the same target tracking accuracy.

CrowdTracker:一种基于移动群智感知的目标跟踪方法

面向目标跟踪问题提出一种基于移动群智感知的解决方案CrowdTracker.不同于基于视频监控的目标跟踪方法,通过基于群智的多人协作拍照方式实现对移动目标的轨迹预测和跟踪,其优化目标为在保证准确实时地对目标进行跟踪的同时尽可能地减少用户激励的成本(假设激励与完成任务的参与者人数和参与者完成任务所移动的距离成正比).为实现该目标,提出了目标移动性预测的方法MPRE和任务分配的方法T-centric,P-centric.T-centric是以任务为中心的参与者选择方法,而P-centric是以人为中心的任务选择方法.MPRE通过分析大量的车辆历史轨迹建立城市里车辆的移动模型以预测目标下一步的位置.在预测的区域内通过T-centric或P-centric方法进行跟踪任务分配.通过一个大规模的真实数据集对移动性预测方法MPRE和2种任务分配算法进行实验评估,实验结果表明:CrowdTracker能有效地在实现目标实时跟踪的同时降低激励成本.

Dynamic cluster member selection method for multi-target tracking in wireless sensor network

Multi-target tracking(MTT) is a research hotspot of wireless sensor networks at present.A self-organized dynamic cluster task allocation scheme is used to implement collaborative task allocation for MTT in WSN and a special cluster member(CM) node selection method is put forward in the scheme.An energy efficiency model was proposed under consideration of both energy consumption and remaining energy balance in the network.A tracking accuracy model based on area-sum principle was also presented through analyzing the localization accuracy of triangulation.Then,the two models mentioned above were combined to establish dynamic cluster member selection model for MTT where a comprehensive performance index function was designed to guide the CM node selection.This selection was fulfilled using genetic algorithm.Simulation results show that this method keeps both energy efficiency and tracking quality in optimal state,and also indicate the validity of genetic algorithm in implementing CM node selection.

空地一体化测绘多无人机飞行协同控制方法

针对多无人机在协同编队飞行过程中通信信息利用率低,导致无人机轨迹规划效果差、效率低等问题,本文提出了一种空地一体化测绘多无人机飞行协同控制方法。利用A*算法改进一致性算法,对多个共同飞行的无人机实行任务分配,通过改进的一致性算法实行轨迹规划。根据无人机航向角、飞行高度和速度等信息,对无人机进行编队控制,实现多无人机飞行协同控制。实验结果表明:所提方法避障效果较优,适用于各类对多无人机控制精度要求较高的工作。

非理想检测下多雷达网络节点选择与辐射资源联合优化分配算法

该文针对分布式相控阵多雷达网络的多目标跟踪场景,研究非理想检测条件下的节点选择与辐射资源联合优化分配算法。首先,根据分布式相控阵多雷达网络构成、目标运动模型、雷达量测模型以及雷达节点检测情况,推导非理想检测下以雷达节点选择、辐射功率和信号带宽为变量的贝叶斯克拉默-拉奥下界(BCRLB)闭式解析表达式,并以此作为多目标跟踪精度衡量指标。在此基础上,以最小化系统各雷达节点对所有目标的总辐射功率为优化目标,以满足目标跟踪精度门限以及给定的系统射频辐射资源限制为约束条件,建立非理想检测条件下多雷达网络节点选择与辐射资源联合优化分配模型,对各时刻雷达节点选择、辐射功率和信号带宽等参数进行联合优化设计,以提升多雷达网络的射频隐身性能。最后,针对上述非线性、非凸优化问题,采用基于障碍函数法和循环最小化算法的4步分解算法进行求解。仿真结果表明,与现有算法相比,所提算法能在满足给定多目标跟踪精度的条件下有效降低分布式相控阵多雷达网络的总辐射功率,至少降低了约32.3%,从而提升其射频隐身性能。

一种防空服务质量模型下的集中式多输入多输出雷达三维机动跟踪功率分配方法

针对防空作战中现有多功能雷达功率资源利用率低的问题,提出一种基于服务质量(Quanlity of Service,QoS)模型的三维机动跟踪功率分配方法以差异化标准提升多目标跟踪性能。将目标三维机动模型建立为自适应当前统计模型,通过将加速度协方差与估计误差协方差矩阵相关联以实现自适应调整。在此基础上,对三维跟踪下的贝叶斯克拉美罗下界进行推导,并将其作为跟踪误差衡量指标。通过构建关于目标威胁度与期望跟踪精度的函数关系,建立防空QoS模型下的闭环功率优化分配机制。证明所构建功率优化分配模型是凸优化问题,并进一步转化为半正定规划问题进行求解。仿真结果表明,相对于传统功率分配方法,所提方法能显著提高全局跟踪效能。

组网雷达系统高精度协同跟踪和功率分配方法

分布式组网雷达系统可充分利用多雷达协同优势提升动目标跟踪性能。然而,在实际中,组网雷达系统的发射总功率受限且量测函数的高度非线性,都会导致目标跟踪精度极大受限。针对上述问题,提出一种基于不相关转换滤波(uncorrelated conversion based filter,UCF)的高精度目标协同跟踪和资源管理方法,该滤波方法可充分提取有效量测信息,提升目标状态估计性能,且该信息可作为整体框架的反馈信息进一步优化资源分配。首先推导了后验克拉美罗下界作为优化准则,利用该准则给出当前时刻的最优资源分配;然后基于分配的功率资源,进行目标状态估计;针对强非线性量测函数,提出一种UCF,利用不相关转换提取更多原始量测中的信息,并将其用于线性最小均方误差框架进行状态估计,从而提高目标状态估计性能。仿真结果验证了所提方法的有效性。

面向转向制动工况的制动力动态分配策略

受轮胎侧偏、载荷横移等因素的影响,汽车在转向制动工况下的动力学行为比直线制动更复杂。深入探究该工况下改善汽车循迹能力的制动力分配策略,对于保障行车安全具有重要意义。对此,本文以电子机械制动为载体,进一步研究转向制动工况下的制动力分配策略。首先,以2自由度汽车动力学模型为参考,基于模型预测控制算法求解汽车保持稳定行驶时前和后轴车轮所需的最低侧向力。然后,通过在线求解附着椭圆得出可供各车轮制动的最大纵向力。在此基础上,以最大纵向力为依据进一步计算制动力分配系数,实现制动力优化分配。仿真和试验结果表明:所提出的制动力分配策略能够根据汽车行驶工况、载荷状态和路面附着条件动态调节制动力分配比例,提升了转向制动工况汽车的循迹能力。

基于马尔科夫链的高校学生注意力分配过程研究

为研究高校学生在上课期间的注意力分配情况,使用头戴式眼动仪记录了20名学生的眼动数据。分析表明,瞳孔直径扩大率与测试成绩有显著正相关,说明注意力保持能力影响知识获取和理解水平。学生注视区域分为7个兴趣区:多媒体、黑板、老师、课本、笔记、手机以及其他区域。运用马尔科夫链理论可推算出学生在各兴趣区间内注视的转移概率和稳态分布。研究表明,学生对知识的认知加工主要来源于多媒体和老师板书,低头看课本时最易发生注意转移。该模型实现了高校学生注意力分配的量化研究,为提升教学质量、改善教学模式提供了科学客观的依据。

面向大规模目标跟踪的相控阵雷达资源分配方法

相比于传统雷达,相控阵雷达能够同时生成多个波束并灵活改变波束指向,被广泛应用于多目标跟踪领域。在大规模集群目标协同探测场景中,为支持后续节点对敌方目标进行火力拦截与打击的任务需求,相控阵雷达需要在规定时间内将空域内优先级更高的目标更快地跟踪至火控精度,然而若空域内目标数量过多,雷达探测资源有限,难以完成指定跟踪任务。为了解决这一问题,本文提出了一种面向大规模目标跟踪的相控阵雷达目标分配与功率联合优化算法。首先,推导出包含目标分配和功率优化的预测条件克拉美罗下界,并将其作为目标跟踪性能的衡量指标;随后,本文同时考虑跟踪容量和跟踪精度,以最大化满足跟踪精度的目标数量和最小化多目标优先级加权平均跟踪误差为优化目标,结合相控阵雷达系统资源,建立了大规模目标跟踪下的目标分配和功率联合优化模型,对目标分配变量和发射功率变量进行自适应联合优化配置。针对上述优化问题,本文采用两步分解法,将其分解为目标分配子问题和功率优化子问题,并结合激活函数对非平滑非凸的目标函数进行平滑近似。然后,利用谱投影梯度法进行求解。仿真实验验证了所提算法相较于传统算法在多个场景下均能在指定时间内更快速地将更多目标跟踪至指定精度。

随机数据丢包情况下组网雷达功率分配算法

在组网雷达系统中,由于节点间通信信号传播存在路径衰落等问题,雷达量测数据与资源调度数据的传输接收存在随机性。引入随机丢包变量,用以表征数据接收的随机性,建立丢包状态下组网雷达多目标跟踪功率分配模型,推导了该模型多目标跟踪误差期望下界,最终建立了总功率一定条件下,以最小化目标威胁度加权期望跟踪误差下界的数学优化模型,并通过凸优化算法求解。仿真结果表明,与不考虑随机丢包变量的功率分配算法相比,所提算法在多种场景下均能有效提高目标跟踪精度。

双线束铁路集装箱中心站“列车-轨道”分配策略研究

为了满足铁路集装箱中心站日益增长的作业需求,部分中心站已建成或规划在建第二线束,多线束的建设会大幅提升中心站的作业能力,但相应的也可能会产生线束间的作业均衡和集装箱的场内转运问题。以最小化线束间轨道门吊的工作量差异和集装箱场内转运数量为目标,考虑轨道的时空容量、列车的进出站时间要求及装卸作业要求等约束以保证列车按时完成作业,建立双目标整数规划模型来研究双线束集装箱中心站内的“列车-轨道”分配问题。设计启发式规则生成初始可行解,并利用存档式多目标模拟退火算法处理双目标优化问题,以得到一组帕累托近似最优解。设计算例验证了算法的可行性和有效性,并研究了装卸线的数量和轨道门吊的配置对实验结果的影响。结果表明:本文提出的双线束铁路集装箱中心站“列车-轨道”分配策略能够有效均衡线束间的工作量并降低集装箱场内转运数,灵敏度分析结果能够为未来双线束集装箱中心站的规划建设提供借鉴。

四轮独立驱动电动汽车轨迹跟踪及稳定性协调控制

针对紧急避障轨迹跟踪和车辆稳定性多目标控制问题,以四轮独立驱动智能电动汽车为研究对象,提出一种分层结构的轨迹跟踪及稳定性控制方法,上层控制器采用线性时变模型预测控制(LTV MPC)生成期望前轮转角和附加横摆力矩,并通过PID速度跟踪将车速变化考虑到模型预测优化求解中生成总需求转矩,下层控制器采用二次规划将上层计算的广义力最优分配给四个车轮。其中,预测模型为8自由度车辆模型,被控对象为14自由度车辆模型,并建立联合工况刷子轮胎模型。不同车速,路面附着条件以及有无稳定性控制的双移线工况仿真结果表明,所提控制方法具有较好的鲁棒性和轨迹跟踪性能,极限工况下在保证车辆稳定性的同时提高了轨迹跟踪精度。

面向协同探测的多机雷达功率时间联合优化分配算法

针对多目标搜索及跟踪场景,研究了面向协同探测的多机雷达功率时间联合优化分配算法。首先,基于信号检测理论和克拉美-罗下界,分别推导了雷达搜索性能与跟踪性能评估指标;在此基础上,建立了面向协同探测的多机雷达功率时间联合优化分配模型,即以最大化雷达工作性能指标为优化目标,以满足给定系统资源限制为约束条件,对雷达搜索及跟踪任务中节点选择、辐射功率和任务时间等参数进行联合优化设计;最后,针对上述优化问题,采用基于内点法和粒子群算法的三步分解算法进行求解。仿真结果表明,与现有算法相比,所提算法能够在满足给定系统资源限制的条件下,有效提高雷达系统搜索性能和跟踪精度。