基于Hybrid State A* 与增强安全管道的直升机低空航迹规划

直升机低空突防已逐渐成为现代空战察打任务的核心,而低空航迹规划算法是实现该技术的关键。 尽管现有的航迹规划算法已经被应用于实际低空突防任务,但基于“前端- 后端”式的传统航迹规划算法依然 存在规划航迹机动执行性差与复杂动态场景下易碰撞的缺陷。针对上述问题,本文提出一种基于Hybrid State A* 与增强安全管道的改进算法。首先,基于Hybrid State A* 算法的联合轨迹优化,可以在状态空间中完成兼 顾直升机机动特性的初始航迹高效搜索,有效保证直升机航迹的可达性。其次,基于初始航迹膨胀的增强安全 管道,将后端航迹优化参数限制在安全的可行域内,进而有效提升复杂动态场景下规划航迹的安全性。在实验 环节,本研究结合ROS 机器人仿真环境与Rviz 数据可视化工具完成仿真验证,通过算法间的综合对比实验, 论证了本研究所提算法对规划的航迹机动性与安全性有明显的提升。     

高斯非平稳机动目标波达角模型及跟踪

在实际的跟踪情况中,由于环境条件、目标反射截面等因素的变化,回波信号的功率会随时间变化,即不满足通常阵列信号处理中对高斯信号作平稳性的假设。针对复杂运动条件下高斯非平稳目标的跟踪问题,提出了一种新的机动目标波达角(DOA)模型。该模型全面地刻画了高斯非平稳机动目标的动态,并将目标的DOA和信号功率作为状态变量进行了联合考虑,同时运用虚拟阵列的表示方法构建了相应的观测方程。对于建立的新模型,最后采用无迹卡尔曼滤波(UKF)的框架完成了整个跟踪算法。分析和仿真结果表明,当高斯非平稳机动目标之间存在长时间相互接近的情况时,新方法仍然可以获得较好的跟踪性能。     

一种基于信息一致性的卫星编队协同控制策略

提出一种基于信息一致性的分布式协同控制策略,实现了卫星编队的构型建立、保持与整体机动。首先根据编队各星的初始相对状态,应用一阶一致性算法,协商估计出编队整体系统基准参考点的相对状态;再根据编队整体系统模型预测方程,以凸优化方法规划出期望重构机动路径;之后设计了基于二阶一致性算法的反馈协同控制律,使各星彼此协同地跟踪期望路径与构型、整体机动参考信息,并证明了系统的稳定性。仿真结果表明:只要在基准参考点一致性估计过程中信息拓扑图中存在最大生成树,该策略就能实现卫星编队的协同机动;反馈协同控制律能有效消除相对初始状态误差与地球非球形摄动J2项引起的各星状态偏离,并具有较好的容错性;与传统方法相比,本文方法在燃料消耗总量与均衡性方面均有所改善,且在构型快速建立或重构时,燃料消耗总量方面优势较为明显。     

基于控制变量参数化的主动反拦截突防最优控制计算方法

针对由于敌防空系统防御能力不断提高所带来的进攻导弹突防难题,提出主动反拦截突防(IAIP)的概念,以弥补传统机动突防仅考虑进攻导弹的逃逸而忽略其攻击任务的缺陷。根据IAIP制导的内涵,在综合考虑目标的机动性能、拦截导弹末段的拦截特性及进攻导弹的控制系统性能的基础上,建立进攻导弹-目标-拦截导弹的三体运动模型。将突防制导指令的设计等效为最优控制的求解,其中突防指令为实现燃料最省目标的最优解,进攻导弹的过载、拦截导弹的脱靶量、进攻导弹的攻击角、打击精度和突防后的视线角,分别为控制约束、路径约束和末端约束。借鉴控制变量参数化(CVP)方法将最优控制问题转化为非线性数学规划问题,并将路径约束离散化后采用序列二次规划(SQP)算法得到突防时机给定条件下制导指令的数值解。提出基于CVP的混合遗传算法(CVP-GA),用于求解最优突防时机及制导指令。仿真结果显示,采用IAIP最优控制算法的进攻导弹在成功突防后的打击精度仍可满足任务要求,且其燃料消耗相对于传统串联式突防方法降低了23.7%,验证了该方法的有效性及优越性。     

多UAV路径跟踪协同编队机动指令决策算法

针对多UAV在空间路径跟踪下的协同编队控制问题,设计了一种机动指令决策算法。对单架UAV的路径跟踪引入视距导航算法并进行改进,得到航迹方位角和航迹倾斜角的参考指令;对于多UAV协同编队控制,应用图论知识得到空速大小的参考指令;依据参考指令,通过函数解析与数值编程相结合的方法计算出机动指令。仿真结果表明,该算法能够有效控制多UAV从任意初始状态进行预期的路径跟踪协同编队飞行。     

一种混合的多机动目标跟踪算法研究

提出了一种混合的多机动目标跟踪算法:交互多模型模糊联合概率数据关联算法(IMM-FJPDA),该算法将交互多模型算法(IMM)和模糊联合概率数据关联算法(FJPDA)相结合,它克服了IMM-JPDA算法计算量大和IMM-FDA算法在强杂波环境中跟踪精度差的问题.给出了基于模糊C均值(FCM)算法的多机动目标跟踪步骤.仿真结果表明IMM-FJPDA算法跟踪精度与IMM-JPDA算法相当,但计算量明显减小,提高了跟踪实时性.     

基于辅助粒子滤波的机动目标跟踪研究

针对机动目标跟踪巾扩展卡尔曼算法（EKF）收敛速度慢、跟踪精度低的问题,基于粒子滤波（PF）和辅助粒子滤波（APF）的基本思想,结合目标先验信息将速度约束条件加入到跟踪过程巾,对辅助粒子滤波算法进行了仿真分析,与扩展卡尔曼进行仿真对比,分析了跟踪性能和误差。仿真结果表明,对机动目标跟踪问题,辅助粒子滤波不仅解决了扩展卡尔曼线性化困难难题,与EKF相比还具有收敛速度快,跟踪精度高的优点。     

基于MARKOV过程的反舰导弹突防舰艇编队能力评估

为了全面评估反舰导弹对大型水面舰艇编队的作战能力,引入Markov模型来模拟不同速度、不同高度的导弹对编队的突防效果.以编队的防空队形为例,假定来袭导弹服从Possion分布,编队的拦截系统对来袭导弹采用"先来先服务"的原则,通过设定合理的参数,得出不同速度、不同高度导弹的突防效果.模拟结论从突防效能的角度验证了当前反舰导弹的两种方向:超低空亚声速和低空超声速.所建立的大型水面舰艇编队拦截模型具有普适性,通过修改相应的参数,可以评估其他类编队的拦截作战效果.     

反舰导弹对目标选择的一种新方法

超视距反舰导弹(ASM)在搜索阶段选择预定目标时,目前采用的瞄准点选择方法受自控终点的散布误差和目标机动影响较大。为此提出一种利用舰艇编队相互位置关系(MPR)选择预定目标的方法。编队相互位置关系即其余目标相对参考目标的位置,对于一个编队,选择不同的参考目标得到不同的编队相互位置关系。对发射前装订的编队,以预定目标为参考目标获得其编队相互位置关系,在末制导雷达探测到的编队目标中,若以某目标为参考目标时的编队相互位置关系与之最相似,则该目标即为所要选择的预定目标。该方法不受自控终点散布误差和编队目标整体机动的影响。仿真实验表明该方法正确选择预定目标的概率高于瞄准点选择方法。     

低空飞行特点及其机动性能的分析

低空和超低空突防飞行是目前世界各国航空兵部队经常采用的主要战术飞行之一。本文从低空飞行的主要特点出发,根据世界和我国的飞行实践,较详细地分析了低空和超低空机动飞行的有关问题,如速度的选择、水平和垂直机动性以及纵向杆力的变化特点等,最后对保证飞行安全、发挥超低空突防的能力提出了看法。     

雷达低空目标俯仰角跟踪的C2算法研究

在单脉冲测角体制下,由于多径回波信号的干扰,极大地降低了雷达低空目标仰俯角跟踪精度,甚至丢失目标。通过对多路径反射环境模型分析,得出了岸、海基单脉冲雷达低空目标跟踪时仰俯角测量误差的产生原因,提出将传统的多目标分辨算法(C2算法)应用于低角多径环境下目标俯仰角的跟踪测量,并在不同多径反射环境下对不同高度、不同飞行速度和飞行方向的目标进行了仿真,得到良好的仿真结果,表明该算法可较大地提高俯仰角跟踪测量精度。通过对仿真结果的分析,验证了该算法在低空目标跟踪中的有效性和可行性。     

基于模板匹配的集中式多传感器群内目标精细跟踪算法

为解决多传感器探测下群内目标精细跟踪的难题,基于非机动情况下各探测周期内群内目标真实回波位置相对固定的特性,提出了一种基于模板匹配的集中式多传感器群内目标精细跟踪算法。该算法通过预关联成功的群状态集合与群量测集合分别建立模板形状矩阵和待匹配形状矩阵,利用匹配搜索模型和匹配矩阵确认规则选出代价最小的匹配矩阵,并基于模板和对应的匹配矩阵利用 kalman滤波完成群内各目标航迹的状态更新。仿真表明,与传统多传感器多目标跟踪算法中性能优越的基于数据压缩的集中式多传感器多假设算法相比,该算法在跟踪精度、实时性、有效跟踪率方面的性能明显优越,能很好的满足群内目标精细跟踪的实际工程需求。     

Tracking in Clutter using IMM-IPDA?Based Algorithms

We describe three single-scan probabilistic data association (PDA) based algorithms for tracking manoeuvering targets in clutter. These algorithms are derived by integrating the interacting multiple model (IMM) estimation algorithm with the PDA approximation. Each IMM model a posteriori state estimate probability density function (pdf) is approximated by a single Gaussian pdf. Each algorithm recursively updates the probability of target existence, in the manner of integrated PDA (IPDA). The probability of target existence is a track quality measure, which can be used for false track discrimination. The first algorithm presented, IMM-IPDA, is a single target tracking algorithm. Two multitarget tracking algorithms are also presented. The IMM-JIPDA algorithm calculates a posteriori probabilities of all measurement to track allocations, in the manner of the joint IPDA (JIPDA). The number of measurement to track allocations grows exponentially with the number of shared measurements and the number of tracks which share the measurements. Therefore, IMM-JIPDA can only be used in situations with a small number of crossing targets and low clutter measurement density. The linear multitarget IMM-IPDA (IMM-LMIPDA) is also a multitarget tracking algorithm, which achieves the multitarget capabilities by integrating linear multitarget (LM) method with IMM-IPDA. When updating one track using the LM method, the other tracks modulate the clutter measurement density and are subsequently ignored. In this fashion, LM achieves multitarget capabilities using the number of operations which are linear in the: number of measurements and the number of tracks, and can be used in complex scenarios, with dense clutter and a large number of targets.     

Tracker and signal processing for the benchmark problem with unresolved targets

Radar signal processing is particularly important in tracking closely spaced targets and targets in the presence of sea-surface-induced multipath. Closely spaced targets can produce unresolved measurements when they occupy the same range cell of the radar. These issues are the salient features of the benchmark problem for tracking unresolved targets combined with radar management, for which this paper presents the only complete solution to date. In this paper a modified version of a recently developed maximum likelihood (ML) angle estimator, which can produce two measurements from a single (unresolved) detection, is presented. A modified generalized likelihood ratio test (GLRT) is also described to detect the presence of two unresolved targets. Sea-surface-induced multipath can produce a severe bias in the elevation angle measurement when the conventional monopulse ratio angle extractor method is used. A modified version of a recently developed ML angle extractor, which produces nearly unbiased elevation angle measurements and significantly improves the track accuracy, is presented. Efficient radar resource allocation algorithms for two closely spaced targets and targets flying close to the sea surface are also presented. Finally, the IMMPDAF (interacting multiple model estimator with probabilistic data association filter modules) is used to track these targets. It is found that a two-model IMMPDAF performs better than the three-model version used in the previous benchmark. Also, the IMMPDAF with a coordinated turn model works better than the one using a Wiener process acceleration model. The signal processing and tracking algorithms presented here, operating in a feedback manner, form a comprehensive solution to the most realistic tracking and radar management problem to date.     

Track labeling and PHD filter for multitarget tracking

Multiple target tracking requires data association that operates in conjunction with filtering. When multiple targets are closely spaced, the conventional approaches (as, e.g., MHT/assignment) may not give satisfactory results. This is mainly because of the difficulty in deciding what the number of targets is. Recently, the probability hypothesis density (PHD) filter has been proposed and particle filtering techniques have been developed to implement the PHD filter. In the particle PHD filter, the track labeling problem is not considered, i.e., the PHD is obtained only for a frame at a time, and it is very difficult to perform the multipeak extraction, particularly in high clutter environments. A track labeling method combined with the PHD approach, as well as considering the finite resolution, is proposed here for multitarget tracking, i.e., we keep a separate tracker for each target, use the PHD in the resolution cell to get the estimated number and locations of the targets at each time step, and then perform the track labeling ("peak-to-track" association), whose results can provide information for PHD peak extraction at the next time step. Besides, by keeping a separate tracker for each target, our approach provides more information than the standard particle PHD filter. For example, in group target tracking, if we are interested in the motion of a specific target, we can track this target, which is not possible for the standard particle PHD filter, since the standard particle PHD filter does not keep track labels. Using our approach, multitarget tracking can be performed with automatic track initiation, maintenance, spawning, merging, and termination     

一种基于扩展模型的隐身目标检测前跟踪方法

现有的检测前跟踪算法对高分辨雷达隐身目标模型适应性不强,从而容易导致跟踪发散。针对该问题,把粒子滤波与检测前跟踪算法相结合应用于扩展隐身目标的检测跟踪,提出了一种隐身目标扩展模型检测前跟踪方法。首先,采用扩展模型对目标的扩展属性进行假设检验,从而判断目标是否为扩展目标;然后,把目标扩展长度引入状态向量,进行基于扩展模型的隐身目标检测前跟踪（Track-before-detect,TBD）,克服粒子滤波易发散的不足,实现对目标空间长度的估计。仿真结果表明,该算法能够准确判断目标的扩展属性并进行有效的检测跟踪。     

A class of near optimal JPDA algorithms

The crucial problem in multiple target tracking is the hit-to-track data association. A hit is a received signal from a target or background clutter which provides positional information If an incorrect hit is associated with a track, that track could diverge and prematurely terminate or cause other tracks to also diverge. Most methods for hit-to-track data association fall into two categories: multiple hypothesis tracking (MHT) and joint probabilistic data association (JPDA). Versions of MHT use all or some reasonable hits to update a track and delay the decision on which hit was correct. JPDA uses a weighted sum of the reasonable hits to update a track. These weights are the probability that the hit originated from the target in track. The computational load for the joint probabilities increases exponentially as the number of targets increases and therefore, is not an attractive algorithm when expecting to track many targets. Reviewed here is the JPDA filter and two simple approximations of the joint probabilities which increase linearly in computational load as the number of targets increase. Then a new class of near optimal JPDA algorithms is introduced which run in polynomial time. The power of the polynomial is an input to the algorithm. This algorithm bridges the gap in computational load and accuracy between the very fast simple approximations and the efficient optimal algorithms     

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.     

一类新型的机动目标跟踪算法

针对机动目标跟踪,提出了基于截断正态概率模型的改进自适应目标跟踪算法,该算法具有结构和计算简单,鲁棒性好的特点,通过仿真结果对比,充分说明了文中所提出的跟踪算法能够较好地弥补传统的Kalman滤波方法在跟踪机动目标时的不足。