基于多目标算法的空中作战任务规划框架研究

空中作战任务规划是一项复杂的任务，随着空中作战飞行器的种类、数量及其之间交互性的增加，任务规划也变得越来越复杂。任务规划人员必须在有限的时间内制定出最优的任务分配策略。决策支持工具可以辅助任务规划人员找到最优的规划方案。介绍了设计多目标进化算法以及在空中作战任务规划领域的框架和工作流程，具体的任务包括空中打击动态目标的定位问题和情报监视侦察（ISR）任务规划。总结了这些研究的经验教训，探讨了未来可能的发展方向。     

Multi-UAV reconnaissance task allocation for heterogeneous targets using an opposition-based genetic algorithm with double-chromosome encoding

This paper presents a novel multiple Unmanned Aerial Vehicles (UAVs) reconnaissance task allocation model for heterogeneous targets and an effective genetic algorithm to optimize UAVs’ task sequence. Heterogeneous targets are classified into point targets, line targets and area targets according to features of target geometry and sensor’s field of view. Each UAV is regarded as a Dubins vehicle to consider the kinematic constraints. And the objective of task allocation is to minimize the task execution time and UAVs’ total consumptions. Then, multi-UAV reconnaissance task allocation is formulated as an extended Multiple Dubins Travelling Salesmen Problem (MDTSP), where visit paths to the heterogeneous targets must meet specific constraints due to the targets’ feature. As a complex combinatorial optimization problem, the dimensions of MDTSP are further increased due to the heterogeneity of targets. To efficiently solve this computationally expensive problem, the Opposition-based Genetic Algorithm using Double-chromosomes Encoding and Multiple Mutation Operators (OGA-DEMMO) is developed to improve the population variety for enhancing the global exploration capability. The simulation results demonstrate that OGA-DEMMO outperforms the ordinary genetic algorithm, ant colony optimization and random search in terms of optimality of the allocation results, especially for large scale reconnaissance task allocation problems.     

基于遗传算法的无人机协同侦察航路规划

无人机将成为侦察卫星、有人驾驶侦察机的重要补充与增强手段 ,成为未来战场上广泛应用的一种侦察工具。为了提高无人机 (UAV)的侦察效率 ,在执行侦察任务前必需规划设计出高效的无人机侦察飞行航路。针对这一问题 ,本文提出了一种侦察效率指标评估的计算方法 ,解决了航路规划中的侦察效率量化问题。考虑在大范围任务区域内进行侦察航路优化存在计算的复杂性和收敛性等问题 ,本文采用遗传算法对侦察航路进行了优化处理。通过该方法得到的侦察航路可以有效地提高无人机的侦察效率。     

Multivehicle reconnaissance route and sensor planning

A novel approach is described for the real-time coordination of multivehicle teams to perform reconnaissance and surveillance missions. Our approach incorporates a powerful algorithmic framework, which we have used to develop dynamic route and sensor planning algorithms for multivehicle team coordination. Furthermore, this framework allows the development of a class of algorithms to match the extent of communication and coordination permissible for the particular mission. We have implemented a prototype of this framework and presented results for the case when full coordination between the teammates is allowed.     

Effective control allocation using hierarchical multi-objective optimization for multi-phase flight

For different flight phases in an overall flight mission, different control and allocation preferences should be pursued considering lift, drag or maneuverability characteristics. The multi-objective flight control allocation problem for a multi-phase flight mission is studied. For an overall flight mission, different flight phases namely climbing, cruise, maneuver and gliding phases are defined. Firstly, a multi-objective control allocation problem considering drag, lift or control energy preference is constructed. Secondly, considering different control preferences at different flight phases, the analytic hierarchical process method is used to construct a comprehensive performance index from different objectives such as lift or drag preferences. The active set based dynamic programming optimization method is used to solve the real-time optimization problem. For the validation, the Innovative Control Effector (ICE) tailless aircraft nonlinear model and the angular acceleration measurements based adaptive Incremental Backstepping (IBKS) are used to construct the validation platform. Finally, an overall flight mission is simulated to demonstrate the efficiency of the proposed multi-phase and multi-objective flight control allocation method. The results show that the comprehensive performance index for different phases, which are determined from the Analytic Hierarchy Process (AHP) method, can suitably satisfy the preference requirements for different flight phases.     

通信约束下异构多无人机任务分配方法

针对异构多无人机协同执行侦察和打击任务中,存在通信距离、时间延迟等约束条件下的局部任务分配问题,提出了一种基于合同网的分布式多无人机任务分配方法。首先建立了异构集群发现新目标时的局部任务分配问题模型,设计了局部无人机通信网络中的信息一致性算法,实现了任务分配过程中任务发布阶段各无人机的冲突消解。设计了任务分配过程中的联盟构建和无人机资源管理方法,使联盟中各无人机能够以更加平衡的方式消耗资源。仿真结果表明,该方法能够解决通信约束下,异构多无人机执行察打任务时,所触发的针对目标打击任务的任务分配问题,且能够获得最大的系统效能。     

基于滚动窗的元胞自动机实时航迹规划方法研究

为满足载机实时航迹规划中威胁环境不确定性和规划算法实时性的要求,基于预测控制滚动优化的思想和元胞自动机理论,提出一种适合载机实时航迹规划的算法一滚动窗元胞自动机实时航迹规划方法.仿真结果表明,该方法能够在预先未知威胁的飞行环境中有效规划出动态航迹.     

重访机制驱动的多无人机协同动目标搜索方法

为实现多无人机高效捕获灰色任务区域内的移动目标,考虑传感器探测概率与虚警概率,提出了重访机制驱动的协同搜索规划（RMD-CSP）方法,以降低目标遗漏与误判概率。考虑无人机飞行性能约束,以最大化任务执行效能为目标建立多无人机协同搜索模型。根据目标先验信息初始化环境搜索信息图（包括目标概率分布图、环境不确定度图与环境搜索状态图）,利用无人机实时探测信息,基于贝叶斯准则持续更新搜索信息图。定制基于环境不确定度更新的重访机制,通过增加长时间未被重访区域的环境不确定度,引导无人机搜索该区域,降低移动目标的遗漏概率;定制基于目标函数权重更新的重访机制,引导无人机快速重访发现新的疑似目标的区域,对疑似目标进行再次确认,减少由于传感器虚警概率造成的目标误判概率。采用滚动时域规划架构,将搜索规划问题分解为一系列短时域规划问题,提升了求解效率。在典型任务想定下,通过数值仿真试验验证了所提方法的有效性。仿真结果表明,RMD-CSP能够在秒级时间内生成每个时域的搜索航迹,相比于光栅式搜索方法与标准的概率启发式搜索方法,能够引导无人机捕获更多的移动目标,同时减少误判次数,有效提升了多无人机协同搜索的任务效能。     

Effective local dynamic routing strategy for air route networks

With the rapid development of air transportation, network service ability has attracted a lot of attention in academe. Aiming to improve the throughput of the air route network(ARN), we propose an effective local dynamic routing strategy in this paper. Several factors, such as the routing distance, the geographical distance and the real-time local traffic, are taken into consideration.When the ARN is in the normal free-flow state, the proposed strategy can recover the shortest path routing(SPR) strategy. When the ARN undergoes congestion, the proposed strategy changes the paths of flights based on the real-time local traffic information. The throughput of the Chinese air route network(CARN) is evaluated. Results confirm that the proposed strategy can significantly improve the throughput of CARN. Meanwhile, the increase in the average flying distance and time is tiny. Results also indicate the importance of the distance related factors in a routing strategy designed for the ARN.     

Distributed intelligent self-organized mission planning of multi-UAV for dynamic targets cooperative search-attack

This article studies the cooperative search-attack mission problem with dynamic targets and threats, and presents a Distributed Intelligent Self-Organized Mission Planning (DISOMP) algorithm for multiple Unmanned Aerial Vehicles (multi-UAV). The DISOMP algorithm can be divided into four modules: a search module designed based on the distributed Ant Colony Optimization (ACO) algorithm, an attack module designed based on the Parallel Approach (PA) scheme, a threat avoidance module designed based on the Dubins Curve (DC) and a communication module designed for information exchange among the multi-UAV system and the dynamic environment. A series of simulations of multi-UAV searching and attacking the moving targets are carried out, in which the search-attack mission completeness, execution efficiency and system suitability of the DISOMP algorithm are analyzed. The simulation results exhibit that the DISOMP algorithm based on online distributed down-top strategy is characterized by good flexibility, scalability and adaptability, in the dynamic targets searching and attacking problem.     

多无人机协同覆盖路径规划

多无人机协同覆盖路径规划(CPP)由于其并行性和容错能力,对于提高无人机完成侦察、监视、搜索等任务的效率具有重要意义。提出了一种基于无人机任务性能评价和任务区域划分的多无人机协同CPP算法。定量分析了无人机执行覆盖任务的能力,根据无人机及携带成像传感器的性能给出了计算无人机任务性能指数的数学公式;提出了一种基于任务性能和子区域宽度的任务区域划分算法,使无人机的总转弯次数达到最少。仿真结果表明,所提出的CPP算法能够规划出全局最优的多无人机协同覆盖路径。     

Automatic Target Recognition: Some Considerations

Machine analysis of aerial reconnaissance data is desirable. New acquisition techniques, equipment, and sensors, plus a growing demand for rapidly obtained, accurate information necessitate machine performance of tasks presently accomplished by observers and interpreters. Many of these tasks can be described as pattern recognition or information processing functions. This paper discusses significant problems encountered when attempting to identify objects or regions automatically from aerial photography. Problems include sensor distortions, variations in target environment, and concepts of design and ?teaching? of an actual device. Operational requirements such as processing speed and flexibility restrict the types of solutions available. Approaches to target recognition with relative merits and shortcomings are presented; these include shape correlation, feature extraction, and image quantization. The effect of mission requirements on system parameters is discussed. A bibliography is included.     

深空探测器自主技术发展现状与趋势

深空探测器距离地球远、所处环境复杂、苛刻,利用地面测控站进行深空探测器的遥测和遥控已经很难满足探测器控制的实时性和安全性要求。深空探测器自主技术即通过在探测器上构建一个智能自主管理软件系统,自主地进行工程任务与科学任务的规划调度、命令执行、星上状态的监测与故障时的系统重构,完成无人参与情况下的探测器长时间自主安全运行,自主技术已经逐渐成为深空探测领域未来发展的一项关键技术。本文首先分析了传统测控模式对深空探测的约束,回顾了深空探测器自主技术发展的现状,分析了实现深空探测器自主运行的关键技术,包括在轨自主管理系统设计技术、自主任务规划技术、自主导航与控制技术、自主故障处理技术和自主科学任务操作技术。然后结合深空探测工程实施和技术发展需求,提出未来深空探测器自主技术发展的趋势和重点。     

An intelligent target detection method of UAV swarms based on improved KM algorithm

Complete and efficient detection of unknown targets is the most popular application of UAV swarms. Under most situations, targets have directional characteristics so that they can only be successfully detected within specific angles. In such cases, how to coordinate UAVs and allocate optimal paths for them to efficiently detect all the targets is the primary issue to be solved. In this paper, an intelligent target detection method is proposed for UAV swarms to achieve real-time detection requirements. First, a target-feature-information-based disintegration method is built up to divide the search space into a set of cubes. Theoretically, when the cubes are traversed, all the targets can be detected. Then, a Kuhn-Munkres (KM)-algorithm-based path planning method is proposed for UAVs to traverse the cubes. Finally, to further improve search efficiency, a 3D real-time probability map is established over the search space which estimates the possibility of detecting new targets at each point. This map is adopted to modify the weights in KM algorithm, thereby optimizing the UAVs’ paths during the search process. Simulation results show that with the proposed method, all targets, with detection angle limitations, can be found by UAVs. Moreover, by implementing the 3D probability map, the search efficiency is improved by 23.4%–78.1%.     

Distributed online mission planning for multi-player space pursuit and evasion

There are three important roles in evasion conflict: pursuer, target and defender. Pur-suers’ mission is to access targets;targets’ mission is to escape from pursuers’ capture;defenders’ mission is to intercept pursuers who are potentially dangerous to targets. In this paper, a distributed online mission plan (DOMP) algorithm for pursuers is proposed based on fuzzy evaluation and Nash equilibrium. First, an integrated effectiveness evaluation model is given. Then, the details of collaborative mission planning which includes the co-optimization of task distributing, trajectory and corresponding maneuvering scheme are presented. Finally, the convergence and steadiness of DOMP are discussed with simulation results. Compared with centralized mission planning, DOMP is more robust and can greatly improve the effectiveness of pursuing. It can be applied to dynamic scenario due to its distributed architecture.     

深空探测器自主技术发展现状与趋势简

 深空探测器距离地球远、所处环境复杂、苛刻,利用地面测控站进行深空探测器的遥测和遥控已经很难满足探测器控制的实时性和安全性要求。深空探测器自主技术即通过在探测器上构建一个智能自主管理软件系统,自主地进行工程任务与科学任务的规划调度、命令执行、星上状态的监测与故障时的系统重构,完成无人参与情况下的探测器长时间自主安全运行,自主技术已经逐渐成为深空探测领域未来发展的一项关键技术。本文首先分析了传统测控模式对深空探测的约束,回顾了深空探测器自主技术发展的现状,分析了实现深空探测器自主运行的关键技术,包括在轨自主管理系统设计技术、自主任务规划技术、自主导航与控制技术、自主故障处理技术和自主科学任务操作技术。然后结合深空探测工程实施和技术发展需求,提出未来深空探测器自主技术发展的趋势和重点。     

面向无人机蜂群的航电云多层任务调度模型

在航空作战体系中，基于航电云的无人机（UAV）蜂群作战是提高未来无人机综合作战能力的一种新模式。针对无人机蜂群作战的航电云架构，如何将云端作战任务派发到无人机且保证作战任务完成时间是其中关键。在无人机蜂群分层分簇网络结构和模块级资源虚拟化的基础上，对传统单层平台级任务调度模型进行改进，提出了一种细化到模块级的多层任务调度模型，将作战任务从云端逐层调度到无人机功能模块上执行。利用OMNeT++对无人机蜂群多层任务调度模型以及传统的单层任务调度模型分别进行仿真，云端以攻击使命组为例构建使命组集进行分配，并对任务吞吐量、消息平均端到端延时和任务完成时间进行性能对比。仿真结果表明：与平台级单层任务调度相比，在执行任务方面，模块级多层任务调度模型将单个任务平均完成时间降低了46.2%，将使命组完成时间降低了52.1%，在保证任务吞吐量的基础上具有对复杂任务更稳定的调度能力；在网络性能方面，模块级多层任务调度模型消息端到端延时更低，延时分布更集中，提高了网络消息传输的实时性。     

无人直升机任务规划系统研究与实践

无人直升机任务规划系统对于完成超视距侦察、监控能力和打击是非常重要的.本文分析了无人直升机任务规划系统的的基本体系结构和功能模块,研究了无人直升机任务规划系统的建模技术及其优化算法,并在V750无人直升机上进行了验证.     

On Optimal Allocation and Guidance Laws for Linear lnterception and Rendezvous Problems

This paper considers the problem of guiding N interceptors so that they intercept or rendezvous with N targets at the same time T. It is assumed that the interceptors and targets are described by linear time-varying differential equations (deterministic or stochastic). Under certain assumptions both the optimal (static or dynamic) interceptor-target allocation and the guidance of each interceptor for the optimal allocation can be obtained with modest real-time and storage computer requirements.     

Directed Subspace Search ML-PDA with Application to Active Sonar Tracking

The maximum likelihood probabilistic data association (ML-PDA) tracking algorithm is effective in tracking Very Low Observable targets (i.e., very low signal-to-noise ratio (SNR) targets in a high false alarm environment). However, the computational complexity associated with obtaining the track estimate in many cases has precluded its use in real-time scenarios. Previous ML-PDA implementations used a multi-pass grid (MPG) search to find the track estimate. Two alternate methods for finding the track estimate are presented-a genetic search and a newly developed directed subspace (DSS) search algorithm. Each algorithm is tested using active sonar scenarios in which an autonomous underwater vehicle searches for and tracks a target. Within each scenario, the problem parameters are varied to illustrate the relative performance of each search technique. Both the DSS search and the genetic algorithm are shown to be an order of magnitude more computationally efficient than the MPG search, making possible real-time implementation. In addition, the DSS search is shown to be the most effective technique at tracking a target at the lowest SNR levels-reliable tracking down to 5 dB (postprocessing SNR in a resolution cell) using a 5-frame sliding window is demonstrated, this being 6 dB better than the MPG search.