Event-triggered cooperative target tracking in wireless sensor networks

Since the issues of low communication bandwidth supply and limited battery capacity are very crucial for wireless sensor networks, this paper focuses on the problem of event-triggered cooperative target tracking based on set-membership information filtering. We study some fundamental properties of the set-membership information filter with multiple sensor measure-ments. First, a sufficient condition is derived for the set-membership information filter, under which the boundedness of the outer ellipsoidal approximation set of the estimation means is guaranteed. Second, the equivalence property between the parallel and sequential versions of the set-membership information filter is presented. Finally, the results are applied to a 1D event-triggered target tracking scenario in which the negative information is exploited in the sense that the measurements that do not satisfy the triggering conditions are modelled as set-membership mea-surements. The tracking performance of the proposed method is validated with extensive Monte Carlo simulations.     

Use of map information for tracking targets on airport surface

A generic and novel approach for integrating airport map information with sensor measurements in the track estimation process is proposed and evaluated. The surface restrictions imposed by the network of roads, taxiways, and runways, represented by a simplified geometric model, are included in both the target observation and the dynamic models, to derive the target state estimates. The performance of the methods proposed is illustrated in representative airport surface scenarios, taking as a reference for comparison other tracking alternatives such as VS-IMM (variable structure interacting multiple model estimator) ground target tracking, or standard ones that do not make use of ground information.     

A modified forward and backward reaching inverse kinematics based incremental control for space manipulators

Forward and backward reaching inverse kinematics (FABRIK) is an efficient two-stage iterative solver for inverse kinematics of spherical-joint manipulator without the calculation of Jacobian matrix. Based on FABRIK, this paper presents an incremental control scheme for a free-floating space manipulator consists of revolute joints and rigid links with the consideration of joint constraints and dynamic coupling effect. Due to the characteristics of FABRIK, it can induce large angular movements on specific joints. Apart from that, FABRIK maps three dimensional (3D) problem into two dimensional (2D) problem by a simple geometric projection. This operation can cause infinite loops in some cases. In order to overcome these issues and apply FABRIK on space manipulators, an increments allocation method is developed to constrain the angular movements as well as to re-orient the end-effector. The manipulator is re-positioned based on the momentum conservation law. Instead of pure target position tracking, the orientation control of the end-effector is also considered. Numerical simulation is performed to testify and demonstrate the effectiveness and reliability of the proposed incremental control approach.     

微型飞行器过失速降落轨迹跟踪控制设计

采用基于张量积的T-S模糊建模与控制方法,对微型飞行器过失速降落纵向运动的轨迹跟踪控制问题进行了研究。首先,建立了过失速降落中飞行器纵向运动的线性变参数动力学模型;然后,通过张量积模型转化方法,将线性变参数模型转化为张量积胞体模型,基于该模型设计了并行分布补偿控制器,用Lyapunov稳定性理论证明了闭环系统的稳定性,并推导了区域极点配置条件以获得更好的暂态响应性能;最后,对飞行器的过失速降落过程进行了仿真,验证了所设计的跟踪控制器的有效性。     

一种无人机定距盘旋跟踪制导律及稳定性证明

对地面目标的定距盘旋跟踪问题是无人机（UAVs）在任务应用阶段需要面临的重要问题之一,如何在传感器信息受限的情况下完成跟踪任务是目前的研究热点。首先针对地面固定目标设计了一种仅依赖测距传感器的制导律,不再需要传统的视线角信号以及目标和无人机自身的定位信息;其次,设计了李雅普诺夫函数对该制导律的稳定性进行了严格数学证明;最后,将该制导律推广到对地面匀速和变速移动目标的跟踪制导。相比于现有制导律,所提出的制导律结构更为简洁,仅有一个设计参数,并且制导策略更为合理。仿真结果表明所提出的制导律能够实现无人机对地面固定和移动目标的稳定跟踪。     

Bernoulli particle flter with observer altitude for maritime radiation source tracking in the presence of measurement uncertainty

For maritime radiation source target tracking in particular electronic counter measures（ECM）environment,there exists two main problems which can deteriorate the tracking performance of traditional approaches.The frst problem is the poor observability of the radiation source.The second one is the measurement uncertainty which includes the uncertainty of the target appearing/disappearing and the detection uncertainty（false and missed detections）.A novel approach is proposed in this paper for tracking maritime radiation source in the presence of measurement uncertainty.To solve the poor observability of maritime radiation source target,using the radiation source motion restriction,the observer altitude information is incorporated into the bearings-only tracking（BOT）method to obtain the unique target localization.Then the two uncertainties in the ECM environment are modeled by the random fnite set（RFS）theory and the Bernoulli fltering method with the observer altitude is adopted to solve the tracking problem of maritime radiation source in such context.Simulation experiments verify the validity of the proposed approach for tracking maritime radiation source,and also demonstrate the superiority of the method compared with the traditional integrated probabilistic data association（IPDA）method.The tracking performance under different conditions,particularly those involving different duration of radiation source opening and switching-off,indicates that the method to solve our problem is robust and effective.     

基于模糊贝叶斯网的空战传感器资源管理方法

空战中目标状态信息的不确定性、目标与我机相对态势是影响机载传感器资源分配问题的重要影响因素.针对此问题,提出一种基于模糊贝叶斯网(FBN)的空战传感器资源管理方法,以空战传感器资源管理中涉及的影响因素因果关系作为建网依据,将目标信息增量、目标威胁、飞行员指令作为证据变量驱动网络进行概率推理,从而获取空战传感器资源的分配结果.仿真结果表明,与传统方法相比,方法的自适应变间隔采样策略能够根据目标威胁及飞行员指令影响,管理空战态势不同阶段的传感器资源以满足空战作战任务需求.     

低轨星座传感器资源实时调度方法

针对低轨星座传感器资源调度问题,提出了一种基于几何精度衰减因子（GDOP）和目标位置估计误差椭球的调度方法,该方法综合考虑了目标的静态、动态定位因素,优先选择卫星目标视线垂直于误差椭球长轴的传感器资源对目标进行跟踪。仿真结果表明,与已有的仅考虑GDOP的调度方法相比,该方法既节省了传感器资源,又提高了目标的跟踪精度。     

基于Leader-Follower编队的无人机协同跟踪地面目标制导律设计

对地面目标的自动跟踪是无人机在任务应用阶段需要解决的重要问题之一,多无人机协同跟踪能够提高对目标运动状态的估计精度并降低目标丢失的概率,因而具有重要研究意义。本文提出了一种基于Leader-Follower编队的无人机协同跟踪制导方法,解决了传统Standoff跟踪模式对地面目标的速度范围限制问题。首先,通过控制无人机的航向不断趋近于地面目标牵连跟踪圆切线方向的方法设计了Leader无人机自动跟踪地面目标的制导律并完成了稳定性证明;其次,通过控制Follower无人机的速度和航向角逐渐趋近于Leader无人机速度和航向的协同跟踪策略,分别设计了Follower无人机自动跟踪Leader无人机的制导律和编队相位协同制导律并完成了稳定性证明;最后,分别针对静止目标、匀速直线运动目标和变速运动目标的跟踪问题进行了仿真验证,结果表明所提出的制导方法能够实现对不同运动状态地面目标的自动协同跟踪,并且跟踪性能优于基于李雅普诺夫向量法的制导方法。     

基于量测一致性的分布式多传感器多目标跟踪算法

针对无线传感器网络跟踪多目标过程中传感器能搭载的计算负荷有限,不宜采用复杂算法实现数据处理的问题,提出了一种基于量测一致性的分布式多传感器多目标跟踪算法。算法采用计算相对简易的最近邻域法处理多目标跟踪中的数据互联问题,针对最近邻域法容易受杂波干扰的情况,通过量测的平均一致性迭代来改进算法的性能。仿真结果证明,算法具备有效抑制因误判产生的错误量测对跟踪过程干扰的性能,实现了良好的传感器网络跟踪精度和估计信息一致性。     

An improved multiple target angle tracking algorithm

An improved version of a multiple-target-angle tracking algorithm using sensor array outputs is presented. While retaining all the good features of the original algorithm, the improved version greatly reduces the error propagation due to the use of recursive approximations in updating target angle estimates. The assumption of a constant signal covariance matrix is no longer necessary. The improved performance of the proposed algorithm is demonstrated by computer simulations dealing with the tracking of two moving targets     

密集杂波背景下的水下多目标跟踪方法

水下多目标跟踪是水声信号处理领域研究的热点和难点问题。高斯混合概率假设密度（Gaussian mixture probability hypothesis density, GM-PHD）滤波器以其高效的计算效率为解决水下多目标跟踪问题提供了保证。然而，GM-PHD滤波器在跟踪目标时需要先验已知新生目标的强度，否则其性能会出现严重退化。针对该问题，提出一种滑动窗两步初始化高斯混合概率假设密度（sliding window two step initialization GM-PHD, SWTSI-GMPHD）滤波器。将提出的滑动窗两步初始化方法嵌入GM-PHD滤波器，利用滑动窗两步初始化方法估计新生目标强度，减少杂波干扰导致跟踪结果中出现的虚假目标。仿真实验表明，在杂波密集环境下，相较于其他跟踪方法，提出方法将跟踪精度提高69.84%，52.62%和41.05%。     

基于自适应Siamese网络的无人机目标跟踪算法

无人机已被广泛应用到军事和民用领域，目标跟踪是无人机应用的关键技术之一。针对无人机跟踪过程中目标易发生形变、遮挡等问题，提出一种基于自适应Siamese网络的无人机目标跟踪算法。首先，利用2个卷积网络构建一个5层Siamese网络，通过对模板特征与当前帧图像特征进行卷积得到目标位置；其次，利用高斯混合模型对以往的预测结果进行建模并建立目标模板库；然后，从模板库中挑选出最可靠的目标模板并以此更新Siamese网络的匹配模板，使Siamese网络能够自适应目标的外观变化；最后，引入回归模型进一步精确目标位置，降低背景对网络性能的影响。仿真实验结果表明：该算法有效降低了形变、遮挡等情况对跟踪性能的影响，具有较高的准确率。     

Detection of Target Maneuver Onset

A classical maneuvering target tracking (MTT) problem (detection of the onset of a target maneuver) is presented in two parts. The first part reviews most traditional maneuver onset detectors and presents results from a comprehensive simulation study and comparison of their performance. Six algorithms for maneuver onset detection are examined: measurement residual chi-square, input estimate chi-square, input estimate significance test, generalized likelihood ratio (GLR), cumulative sum, and marginalized likelihood ratio (MLR) detectors. The second part proposes two novel maneuver onset detectors based on sequential statistical tests. Cumulative sums (CUSUM) type and Shiryayev sequential probability ratio (SSPRT) maneuver onset detectors are developed by using a likelihood marginalization technique to cope with the difficulty that the target maneuver accelerations are unknown. The proposed technique gives explicit solutions for Gaussian-mixture prior distributions, and can be applied to arbitrary prior distributions through Gaussian-mixture approximations. The approach essentially utilizes a~priori information about the maneuver accelerations in typical tracking engagements and thus allows to improve detection performance as compared with traditional maneuver detectors. Simulation results demonstrating the improved capabilities of the proposed onset maneuver detectors are presented.     

基于LSTM的空间机器人系统惯性张量在轨辨识

在空间机器人抓捕目标的过程中,整个系统的惯性张量会随时间变化且在目标被捕获瞬间发生突变,这会严重影响整体姿态控制的精度。针对以上问题,提出了一种基于长短期记忆（LSTM）的系统惯性张量在轨实时辨识方法。首先,对于目标捕获前后的2个阶段,利用拉格朗日方程建立了空间机器人的动力学模型;然后,基于所建空间机器人模型采用域随机化方法生成足量训练数据,并用其对由LSTM网络与多层全连接网络构建的参数辨识网络进行训练;最后,使用训练好的参数辨识网络对系统惯性张量进行辨识。数值仿真结果表明：所提方法能够精确辨识空间机器人抓捕过程中的系统惯性张量,所研究系统的主惯量平均相对辨识误差小于0.001,惯性积的平均相对辨识误差小于0.01。     

基于决策不确定性的多目标跟踪传感器管理

针对高杂波、电子干扰环境,在量测驱动的多目标滤波框架下提出了一种基于决策不确定性的传感器管理方法。首先,根据部分可观测马尔科夫决策过程的理论,给出了基于Rényi信息增量的传感器管理一般方法。其次,综合考虑决策过程的信息完整性、信息质量、信息的内涵等因素,在量测驱动的自适应滤波框架下,基于目标运动态势评估多目标决策不确定性水平,并选取最大决策不确定性目标。最后,以最大决策不确定性目标的信息增量最大化为准则进行传感器分配方案的求解。仿真实验表明所提方法能够有效抑制电子干扰、杂波对多目标跟踪及传感器分配的影响,与基于威胁的传感器管理方法相比,所提方法的平均最优子模式分配（OSPA）距离及平均计算时长均显著降低,且在高杂波、电子干扰情形下具有较高的可靠性。     

Coordinated target localization base on pseudo measurement for clustered space robot

This paper presents a coordinated target localization method for clustered space robot.According to the different measuring capabilities of cluster members,the master-slave coordinated relative navigation strategy for target localization with respect to slavery space robots is proposed;then the basic mathematical models,including coordinated relative measurement model and cluster centralized dynamics,are established respectively.By employing the linear Kalman flter theorem,the centralized estimator based on truth measurements is developed and analyzed frstly,and with an intention to inhabit the initial uncertainties related to target localization,the globally stabilized estimator is designed through introduction of pseudo measurements.Furthermore,the observability and controllability of stochastic system are also analyzed to qualitatively evaluate the convergence performance of pseudo measurement estimator.Finally,on-orbit target approaching scenario is simulated by using semi-physical simulation system,which is used to verify the convergence performance of proposed estimator.During the simulation,both the known and unknown maneuvering acceleration cases are considered to demonstrate the robustness of coordinated localization strategy.     

雷达和红外成像双传感器信息融合目标识别研究

提出了一种利用目标的雷达和红外成像２种独立的传感器信息的互补性来构造特征向量的信息融合方法——联合向量空间法,并用对应的自适应信息融合系统进行目标识别。仿真证实比用单传感器的效果明显优越,从而说明了本文方法的有效性。     

基于超短基线声传感器阵的高速目标轨迹估计

针对低空高速飞行目标跟踪问题，首先研究了某典型目标噪声信号的时频特性，发现其信号呈现宽带低频特征，难以从频域对目标轨迹进行估计。在此基础上，从各路接收信号的到达时延量入手，考虑到声基阵只能布设于有限空间内的制约，提出了一种基于超短基线阵时延估计的目标跟踪方法。该方法利用各个超短基线阵接收声强极值点分别估计目标运动轨迹垂线方向，计算多个垂线的叉乘向量实现对目标运动方向的估计，再利用多面交汇的方式获估计得到目标运动轨迹。分别对目标俯仰角、方位角及运动轨迹估计的理论误差进行了推导，根据理论估计误差，为能够实现对目标运动轨迹的估计，各个超短基线阵应尽量保证与目标运动轨迹不在同一平面上。根据仿真结果，在采用4个传感器基阵时，角度估计平均误差在4°以内，位置估计相对误差在5%左右。仿真结果验证了该方法的有效性。     

基于扩展卡尔曼滤波的动量轮故障检测方法

 动量轮作为卫星姿态控制系统的关键执行部件,对其故障检测对维持卫星的正常运行具有重要意义。首先对动量轮系统的故障进行分析,在建立动量轮线性离散状态空间模型的基础上,把动量轮的故障检测作为时变参数系统的跟踪来处理,将动量轮的模型参数作为扩展的状态空间中的状态量,使得动量轮物理模型参数与状态空间中的状态量有对应关系,通过在扩展了的状态空间上采用扩展的卡尔曼滤波,完成时变参数的跟踪。然后,将离散空间的状态量变换回连续空间中,利用物理参数与状态量的对应关系,实现对动量轮物理参数的跟踪。此方法物理意义明确,为系统的物理参数提供了定量的估计值,为进一步诊断故障原因提供了良好的基础。数值仿真表明,此方法能够通过同时检测多个故障参量,实现故障的检测并满足卫星实时性要求。