基于小波变换的快速多目标多帧多空间数据关联

该文提出一种基于小波变换的快速多目标多帧多空间数据关联算法。小波变换的引入把数据关联推广到多帧情况。该算法具有不依赖先验知识的特点,在复杂杂波环境下表现出较好的关联效果。算法的关联性能在多被动传感器多目标跟踪系统中进行了评估。仿真实验表明新算法在复杂杂波环境下表现出比联合概率数据关联等算法小的计算复杂度和好的关联效果。     

一个快速的被动式传感器数据关联算法

本文针对仅能获得关于目标角度信息的被动式传感器、且允许传感器漏检、虚警、以及监视空域目标位置和数量未知的静态数据关联问题,提出了一个基于空间交点属性强度的快速数据关联算法.与现有算法相比,本方法较好地改进了数据关联算法的精度和运算速度.最后,通过仿真实验给以验证.     

Evolutionary algorithm for data association and IMM-based target tracking in IR image sequences

Simultaneous tracking of multiple maneuvering and non-maneuvering targets in the presence of dense clutter and in the absence of any a priori information about target dynamics is a challenging problem. A successful solution to this problem is to assign an observation to track for state update known as data association. In this paper, we have investigated tracking algorithms based on interacting multiple model to track an arbitrary trajectory in the presence of dense clutter. The novelty of the proposed tracking algorithms is the use of genetic algorithm for data association, i.e., observation to track fusion. For data association, we examined two novel approaches: (i) first approach was based on nearest neighbor approach and (ii) second approach used all observations to update target state by calculating the assignment weights for each validated observation and for a given target. Munkres’ optimal data association, most widely used algorithm, is based on nearest neighbor approach. First approach provides an alternative to Munkres’ optimal data association method with much reduced computational complexity while second one overcomes the uncertainty about an observation’s source. Extensive simulation results demonstrate the effectiveness of the proposed approaches for real-time tracking in infrared image sequences.     

杂波环境下被动多传感器机动目标跟踪新算法

针对杂波环境被动传感器机动目标跟踪问题,该文研究了一种基于粒子滤波的被动多传感器机动目标跟踪新算法.在该算法中,首先推导了杂波环境下粒子滤波的似然函数表达式.其次将粒子滤波与交互多模型(IMM)相结合,用IMM方法实现模型的切换,以适应目标的机动变化.用粒子滤波实现对观测方程的非线性处理.最后,建立了被动多传感器的非线性观测模型,避免了目标的不可观测性,并且算法还能够处理非高斯噪声情况.仿真实验结果表明,提出的算法能够有效地对被动机动目标跟踪,且性能优于交互多模型概率数据关联滤波器(IMM-PDAF).     

Robust Neural-Network-Based Data Association and Multiple Model-Based Tracking of Multiple Point Targets

Data association and model selection are important factors for tracking multiple targets in a dense clutter environment without using a priori information about the target dynamic. We propose a neural-network-based tracking algorithm, incorporating a interacting multiple model and show that it is possible to track both maneuvering and nonmaneuvering targets simultaneously in the presence of dense clutter. Moreover, it can be used for real-time application. The proposed method overcomes the problem of data association by using the method of expectation maximization and Hopfield network to evaluate assignment weights. All validated observations are used to update the target state. In the proposed approach, a probability density function (pdf) of an observed data, given target state and observation association, is treated as a mixture pdf. This allows to combine the likelihood of an observation due to each model, and the association process is defined to incorporate an interacting multiple model, and consequently, it is possible to track any arbitrary trajectory     

Bearings-only maneuvering target tracking based on fuzzy clustering in a cluttered environment

This paper proposes a novel bearings-only maneuvering target tracking algorithm based on maximum entropy fuzzy clustering in a cluttered environment. In the proposed algorithm, the interacting multiple model (IMM) approach is used to solve the maneuvering problem of target, and the false alarms generated by clutter are accommodated through a probabilistic data association filter (PDAF). To reduce the computational load, the association probability is substituted by fuzzy membership degree provided by a modified version of fuzzy clustering algorithm based on maximum entropy principle, and the “maximum validation distance” is also defined based on the discrimination factor, which enables the algorithm eliminate invalid measurements. Moreover, to avoid the unobservability problem of passive target tracking, a nonlinear measurement model of multiple passive sensors is formulated. Finally, simulation results show that the proposed algorithm has advantages over the conventional IMM-PDAF algorithm in terms of simplicity and efficiency.     

杂波环境下多被动传感器单目标跟踪算法

由于被动传感器只能获得目标的角度量测,因此杂波环境下基于被动传感器的关联问题较主动传感器更为困难。针对杂波环境下纯方位多被动传感器系统的单目标跟踪问题,提出了一种基于扩展卡尔曼滤波的模糊综合贴近度关联跟踪方法。该方法采用直角坐标系下多被动传感器系统的扩展卡尔曼滤波对目标进行跟踪。首先利用目标航迹的预测信息,针对每个传感器建立确认跟踪门;在获得候选关联组合后,直接利用角度信息建立各候选关联组合与角度预测值间的模糊综合贴近度,通过在所获得的全部模糊综合贴近度中寻求最优解完成量测到航迹的关联。仿真实验表明,该方法可以有效地解决杂波环境下多被动传感器系统的单目标跟踪问题。     

三维空间被动传感器的快速数据关联算法研究

这篇文章主要研究的是三维被动传感器的静态测量数据关联问题.多维指派算法以及它的诸多近优改进型方法已被证明可有效解决数据关联问题,但它们的缺点是候选关联代价的计算复杂且计算量大.我们提出了一种快速数据关联算法即基准线最小代价算法,该算法实质上是基准线算法与改进的三维指派算法的结合.它的优点是可快速削减候选关联集合的数目,并可简化关联代价的计算.仿真结果表明,该算法的计算量小于标准的三维指派算法.     

多源场景下的无源雷达联合杂波对消技术

针对基于LTE信号的无源雷达杂波对消,由于存在多个同频基站干扰信号,传统的杂波对消方法,即一一对消,滤波器权值收敛误差大,影响杂波对消性能。本文提出了一种基于联合处理模型的杂波对消算法。该算法将多个同频基站信号作为输入,基于LMS算法同时对消所有信号源的直达波和多径杂波;进而通过分块处理减少了迭代次数,可同时在块内利用FFT快速实现,有效减少了计算复杂度。通过仿真分析,验证了算法能够有效避免收敛误差大的问题,为基于LTE信号的无源雷达信号杂波处理提供了新的途径。     

密集杂波环境下的简化JPDA多目标跟踪算法

为简化联合概率数据关联算法(Joint Probabilistic Data Association, JPDA)的计算复杂度,增强JPDA算法的实时性,设计了一种新的JPDA简化算法。首先根据目标航迹与量测之间的关联规则,定义了一种新的计算关联概率的方法,之后分析公共量测对目标的影响,引入公共量测影响因子修正关联概率。该算法不用进行确认矩阵拆分,有效解决了在密集杂波环境下因回波密度增加而造成的计算上的组合爆炸问题。仿真结果表明,简化的JPDA算法能够在保持对目标有效跟踪的情况下,大大缩短计算时间,提高算法的实时性。