基于CPHD的双层粒子滤波目标跟踪算法

常规基于势概率假设密度滤波(Cardinalized Probability Hypothesis Density,CPHD)的粒子滤波(Particle Fil? ter,PF)跟踪算法应用于多目标跟踪时,容易遇到因粒子数量增加而带来的运算效率下降、目标数目估计不准的问题。文章基于常规粒子滤波 CPHD跟踪算法,通过部署双层粒子,提出基于势概率假设密度滤波的双层粒子滤波 (Two-Layer Particle Filter-CPHD,TLPF-CPHD)算法,以便提高目标数目及状态估计精度。仿真实验结果证明,相比于常规 PF-CPHD算法,新算法具有更好的目标数目和状态估计准确性。     

基于改进粒子滤波的多传感器融合空间目标跟踪

为了跟踪空间目标,构建了基于局部粒子滤波器的多传感器融合方法估计空间目标状态。粒子滤波重要采样过程中,设计了基于融合估计的重要密度函数减少粒子贫化问题,并设计基于 McDE(Memetic compact Differ. ential Evolution)重采样策略,通过对粒子的变异与选择等进化操作来解决粒子退化问题。理论推导与仿真结果皆证明方法的有效性。     

基于二次优化的空间碎片多目标交会方案

针对空间碎片多目标交会轨迹设计问题的复杂性,提出一种利用分支定界法进行总体设计和改进粒子群算法进行二次优化的轨迹优化方法。通过碎片状态矩阵的建立和时间网格的划分,将多目标的交会问题转化为整数规划问题,利用分支定界法对总体交会顺序进行第一次优化。在此基础上,以交会时间和等待时间为优化参数,利用改进梯度粒子群算法对具体的单目标交会轨道问题进行第二次优化。仿真结果表明,飞行器与碎片的相对位置和相对速度误差满足精度要求,验证了多目标交会方案的有效性。     

幅度及多普勒信息辅助的多目标跟踪算法

针对现有的多目标跟踪（MTT）算法在强杂波环境下会出现严重的性能衰退,利用目标的幅度及多普勒特征,基于随机有限集（RFS）理论,提出了一种幅度及多普勒信息辅助的δ广义标签多伯努利（ADI-δ-GLMB）滤波器。首先,利用幅度及多普勒信息对目标状态进行扩展,在此基础上建立了新的量测似然函数。然后,基于δ-GLMB滤波器框架推导了新的更新方程。最后,进行仿真验证,结果表明：强杂波下,ADI-δ-GLMB滤波器跟踪性能明显优于δ-GLMB滤波器,估计精度及稳定性更高,且计算量更低;同信息辅助的概率假设密度（PHD）、多目标多伯努利（MeMBer）滤波器相比,ADI-δ-GLMB滤波器状态估计精度更高。     

基于标签多伯努利滤波的衍生目标跟踪算法

针对现有随机有限集(RFS)滤波器在低信噪比环境下对衍生目标跟踪性能严重下降的问题,提出了一种基于Delta扩展标签多伯努利(δ-GLMB)滤波器的改进算法。基于随机集理论和伯努利衍生模型,推导了新的预测方程,并采用了假设裁剪及分组手段和多伯努利近似技术以降低算法的计算量。针对假设增多引起的虚警问题,将多帧平滑思想和算法相结合,利用标签信息对新目标进行回溯处理。仿真结果表明,所提算法能对目标数目进行无偏估计,在低探测概率和强杂波环境下性能明显优于概率假设密度(PHD)算法,计算开销在衍生初始阶段增长快于PHD,目标较分散时低于PHD。     

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%。     

一种TASIMM-UKF导弹状态估计方法

为了提高对机动导弹状态估计的精度和速度,提出了一种三轴分离IMM-UKF(TASIMM-UKF)滤波算法。将导弹运动状态按坐标轴方向进行三轴分离,并基于CV,CA和CJ模型集,采用无迹卡尔曼滤波器并行估计导弹三轴状态信息,有效地解决了导弹不同运动轴间的模型竞争问题。仿真结果表明,该方法能有效提高状态估计精度,缩短了估计时间,对复杂大机动目标状态估计具有良好的性能。     

轨道碎片探测:技术与问题

美国空间司令部的卫星档案和卫星编目,是分析所观测人造卫星群的主要数据源。大多数用户尚不完全了解这些数据的精度、局限和效用。由于目前又有200多个目标丢失了,实际编目的卫星总数已不到6500个。迄今已发表的卫星群分布和空间密度计算结果不足以说明数据库不完善的原因。美国空间司令部的另一数据源是雷达截面(RCS)编目。雷达截面数据容易受到很多因素的影响,这些影响可能导致很大的目标尺寸估计误差,可能达一个数量级甚至更大。由于弹道系数为假定值,且不能非常详尽地建立大气变化模型,因此往往过高地估计了低地球轨道(LEO)卫星碎片的轨道寿命。美国空间监视网(SSN)的个别探测器所提供的卫星数目和特征信息实际上比其整个网的还要多。     

用于非线性跟踪问题的一种新的粒子滤波器

机动目标跟踪系统通常是非线性而且不完全观测的 ,所以问题的关键在于每一时刻的目标机动性都是高度不确定的。提出了一种新的平滑粒子滤波算法 ,该算法在粒子滤波器中加入了对系统模型的概率分布密度的平滑处理 ,从而很好的解决了目标的机动性估计问题。在仿真研究中 ,与辅助粒子滤波器的比较验证了本文算法处理非线性跟踪问题的优越性     

一种红外多传感器对中段弹道空间邻近目标的联合超分辨弹道估计方法

 分析红外焦平面(IR FRA)对中段弹道空间邻近目标(CSO)的成像特点,指出星载红外传感器为实现对空间邻近目标的跟踪必须对其进行超分辨。提出了一种中段弹道空间邻近目标联合超分辨与弹道估计新方法。该方法结合红外焦平面成像模型和中段弹道动力学模型,使得能够同时利用红外多传感器的多帧信息,基于最小二乘准则建立联合超分辨弹道估计目标函数,并分析选择各目标的起始状态参数作为模型参数。针对目标函数的高维非线性特点,推导最小化意义下等价的降维目标函数,采用量子粒子群优化算法最优化该降维目标函数直接求解模型参数,进而计算出各目标的弹道和辐射强度,实现中段弹道空间邻近目标的联合超分辨与弹道估计。仿真结果验证了该方法的有效性,且相比于传统的先单传感器单帧超分辨、然后多传感器多帧测角数据关联与滤波方法,新方法在避免数据关联复杂问题的同时,其弹道估计精度更高、分辨能力更强。     

Cardinality compensation method based on information-weighted consensus filter using data clustering for multi-target tracking

In this paper, a cardinality compensation method based on Information-weighted Consensus Filter(ICF) using data clustering is proposed in order to accurately estimate the cardinality of the Cardinalized Probability Hypothesis Density(CPHD) filter. Although the joint propagation of the intensity and the cardinality distribution in the CPHD filter process allows for more reliable estimation of the cardinality(target number) than the PHD filter, tracking loss may occur when noise and clutter are high in the measurements in a practical situation. For that reason, the cardinality compensation process is included in the CPHD filter, which is based on information fusion step using estimated cardinality obtained from the CPHD filter and measured cardinality obtained through data clustering. Here, the ICF is used for information fusion. To verify the performance of the proposed method, simulations were carried out and it was confirmed that the tracking performance of the multi-target was improved because the cardinality was estimated more accurately as compared to the existing techniques.     

空间物体的若干法律问题研究

对空间物体的概念、特征进行了研究，探讨了空间物体与空间碎片以及航空航天物体之间的关系，从而明确了空间物体的内涵与外延。此外，对空间物体发射国和空间物体的登记制度进行了研究，提出区分不同情况确定发射国和通过协议划分损害赔偿责任的创新观点。     

空间目标光电巡天策略的优化

随着空间目标与碎片数量的激增,传统的基于目标跟踪的空间目标光电监视手段已不能满足需求,近年出现了面向空域的空间目标巡天观测。针对空间目标巡天观测的特点,同时考虑空间目标观测的目标个数和数据量,提出了一种空间目标巡天观测策略,并建立了相应的空间目标光电巡天优化数学模型。模型中通过0-1变量的使用,实现了目标函数和约束条件的线性化,十分有利于大规模问题的求解,能够更好地满足实际需求。仿真计算表明:策略简单,易于实现,并且有很好的效果;通过优化,只需要少量望远镜即能实现大批量空间目标的监视,并保持一定的观测数据量,为空间目标光电监视策略提供了新的思路。     

PHD filters of higher order in target number

The multitarget recursive Bayes nonlinear filter is the theoretically optimal approach to multisensor-multitarget detection, tracking, and identification. For applications in which this filter is appropriate, it is likely to be tractable for only a small number of targets. In earlier papers we derived closed-form equations for an approximation of this filter based on propagation of a first-order multitarget moment called the probability hypothesis density (PHD). In a recent paper, Erdinc, Willett, and Bar-Shalom argued for the need for a PHD-type filter which remains first-order in the states of individual targets, but which is higher-order in target number. In this paper we show that this is indeed possible. We derive a closed-form cardinalized PHD (CPHD) filter, which propagates not only the PHD but also the entire probability distribution on target number.     

Multi-view space object recognition and pose estimation based on kernel regression

The application of high-performance imaging sensors in space-based space surveillance systems makes it possible to recognize space objects and estimate their poses using vision-based methods. In this paper, we proposed a kernel regression-based method for joint multi-view space object recognition and pose estimation. We built a new simulated satellite image dataset named BUAA-SID 1.5 to test our method using different image representations. We evaluated our method for recognition-only tasks, pose estimation-only tasks, and joint recognition and pose estimation tasks. Experimental results show that our method outperforms the state-of-the-arts in space object recognition, and can recognize space objects and estimate their poses effectively and robustly against noise and lighting conditions.     

A measurement-driven adaptive probability hypothesis density filter for multitarget tracking

This paper studies the dynamic estimation problem for multitarget tracking. A novel gating strategy that is based on the measurement likelihood of the target state space is proposed to improve the overall effectiveness of the probability hypothesis density(PHD) filter. Firstly, a measurement-driven mechanism based on this gating technique is designed to classify the measurements. In this mechanism, only the measurements for the existing targets are considered in the update step of the existing targets while the measurements of newborn targets are used for exploring newborn targets. Secondly, the gating strategy enables the development of a heuristic state estimation algorithm when sequential Monte Carlo(SMC) implementation of the PHD filter is investigated, where the measurements are used to drive the particle clustering within the space gate.The resulting PHD filter can achieve a more robust and accurate estimation of the existing targets by reducing the interference from clutter. Moreover, the target birth intensity can be adaptive to detect newborn targets, which is in accordance with the birth measurements. Simulation results demonstrate the computational efficiency and tracking performance of the proposed algorithm.     

An improved multiple model GM-PHD filter for maneuvering target tracking

In this paper, an improved implementation of multiple model Gaussian mixture probability hypothesis density (MM-GM-PHD) filter is proposed. For maneuvering target tracking, based on joint distribution, the existing MM-GM-PHD filter is relatively complex. To simplify the filter, model conditioned distribution and model probability are used in the improved MM-GM-PHD filter. In the algorithm, every Gaussian components describing existing, birth and spawned targets are estimated by multiple model method. The final results of the Gaussian components are the fusion of multiple model estimations. The algorithm does not need to compute the joint PHD distribution and has a simpler computation procedure. Compared with single model GM-PHD, the algorithm gives more accurate estimation on the number and state of the targets. Compared with the existing MM-GM-PHD algorithm, it saves computation time by more than 30%. Moreover, it also outperforms the interacting multiple model joint probabilistic data association (IMMJPDA) filter in a relatively dense clutter environment.     

Joint parameter and state estimation for stochastic uncertain system with multivariate skew t noises

Due to the pulse interference, measurement outliers and artificial modeling errors, the multivariate skew t noise widely exists in the real environment. However, to date, little attention has been paid to the state estimation for systems in which the process noise and the measurement noise are both modeled as the heavy-tailed and skew non-Gaussian noise. In this paper, the multivariate skew t distribution is utilized to model the heavy-tailed and skew non-Gaussian noise. Then a probabilistic gra...     

单天基平台测向的空间非合作目标跟踪

针对仅含角度测量信息的单个天基平台可观测性较弱的问题,提出了一种含脉冲机动检测的空间非合作目标跟踪算法,并设计了非合作目标实时跟踪数据处理流程.该算法利用抗差估计技术和UKF（Unscented Kalman Filter,无迹卡尔曼滤波）算法构造目标跟踪滤波器,并综合残差多项式拟合和新息分布特征等方法实现目标机动检测,在天基平台观测信息类型有限和观测几何较差的情况下,可以同时排除孤立野值和成片测量野值的影响,实现非合作机动目标的连续稳定跟踪.数值实验验证了算法的可行性和有效性,也表明了跟踪精度和可靠性与测量精度密切相关.