共查询到20条相似文献,搜索用时 15 毫秒
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提出一种新的基于压缩感知(Compressive Sensing, CS)处理的序贯扩展卡尔曼滤波(Sequential Extended Kalman Filter, SEKF)方法,以用于脉冲多普勒(Pulse Doppler, PD)雷达机动目标跟踪。利用目标在时延多普勒平面内的稀疏特点建立稀疏量测模型,然后通过压缩采样匹配重构方法获得目标的多普勒量测值,并用SEKF方法进行滤波更新,以改善目标状态的估计性能。在滤波过程中,应用CS处理可改善目标多普勒估计精度,而应用SEKF则可通过加入伪量测减小多普勒量测和目标运动状态之间的非线性误差。仿真实验结果表明,本文所提出的方法和传统的SEKF方法以及已有基于压缩感知的跟踪方法相比对机动目标有更好的跟踪性能。 相似文献
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Tracking highly maneuverable targets with unknown behavior 总被引:2,自引:0,他引:2
Schell C. Linder S.P. Zeider J.R. 《Proceedings of the IEEE. Institute of Electrical and Electronics Engineers》2004,92(3):558-574
Tracking of highly maneuvering targets with unknown behavior is a difficult problem in sequential state estimation. The performance of predictive-model-based Bayesian state estimators deteriorates quickly when their models are no longer accurate or their process noise is large. A data-driven approach to tracking, the segmenting track identifier (STI), is presented as an algorithm that operates well in environments where the measurement system is well understood but target motion is either or both highly unpredictable or poorly characterized. The STI achieves improved state estimates by the least-squares fitting of a motion model to a segment of data that has been partitioned from the total track such that it represents a single maneuver. Real-world STI tracking performance is demonstrated using sonar data collected from free-swimming fish, where the STI is shown to be effective at tracking highly maneuvering targets while relatively insensitive to its tuning parameters. Additionally, an extension of the STI to allow its use in the most common multiple target and cluttered environment data association frameworks is presented, and an STI-based joint probabilistic data association filter (STIJPDAF) is derived as a specific example. The STIJPDAF is shown by simulation to be effective at tracking a single fish in clutter and through empirical results from video data to be effective at simultaneously tracking multiple free-swimming fish. 相似文献
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Bayesian Multi-Object Filtering With Amplitude Feature Likelihood for Unknown Object SNR 总被引:1,自引:0,他引:1
In many tracking scenarios, the amplitude of target returns are stronger than those coming from false alarms. This information can be used to improve the multiple-target state estimation by obtaining more accurate target and false-alarm likelihoods. Target amplitude feature is well known to improve data association in conventional tracking filters, such as probabilistic data association and multiple hypothesis tracking, and results in better tracking performance of low signal-to-noise ratio (SNR) targets. The advantage of using the target amplitude approach is that targets can be identified earlier through the enhanced discrimination between target and false alarms. One of the limitations of this approach is that it is usually assumed that the SNR of the target is known. We show that the reliable estimation of the SNR requires a significant number of measurements, and so we propose an alternative approach for situations where the SNR is unknown. We illustrate this approach in the context of multiple targets for different SNRs in the framework of finite set statistics (FISST). Furthermore, we illustrate how this can be incorporated into approximate multiple-object filters derived from FISST, including probability hypothesis density (PHD) and cardinalized PHD (CPHD) filters. We present simulation results for Gaussian mixture implementations of the filters that demonstrate a significant improvement in performance over just using location measurements. 相似文献
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针对低检测概率下多机动目标的跟踪问题,该文提出一种新的交互式多传感器多目标多伯努利滤波器(IMM-MS-MeMBer)。在IMM-MS-MeMBer滤波器的预测阶段,该文利用当前的量测信息自适应地更新目标的模型概率,并利用更新后的模型概率对目标状态进行混合预测;在IMM-MS-MeMBer滤波器的更新阶段,使用贪婪的多传感器量测划分策略对多传感器量测进行划分,并利用得到的量测划分集合和IMM-MS-MeMBer滤波器对目标的后验概率密度进行更新;除此之外,IMM-MS-MeMBer滤波器能够利用目标的角度和多普勒量测信息同时实现多个机动目标的位置、速度估计。数值实验验证了该文所提IMM-MS-MeMBer滤波器的优越性能。 相似文献
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针对多光电跟踪设备组网后出现的异步测量问题,提出了一种异步分布式序贯目标跟踪算法。该算法由局部滤波器和融合滤波器构成,先利用状态转换方法,将多光电跟踪设备节点及其邻节点的异步测量对齐到融合时刻,得到拟测量方程。随后,利用射影原理对拟测量方程和目标运动状态方程构成的目标跟踪系统,提出异步序贯局部滤波器来计算较为精确的局部滤波值。再以协方差交叉算法为基础,提出基于扩散策略的融合滤波器,对局部估计值进行融合计算,来提高目标跟踪精度,并降低组网后各光电跟踪设备节点融合估计值的差异程度。最后对所提出的算法进行了仿真实验,以验证其有效性。 相似文献
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Zaveri M.A. Merchant S.N. Desai U.B. 《IEEE transactions on systems, man and cybernetics. Part C, Applications and reviews》2007,37(6):1269-1286
We propose an effective technique using a wavelet-based temporal decomposition algorithm to detect single-pixel targets with motion from frame to frame. We next integrate the proposed detection algorithm with an interacting multiple-model method and multiple filter bank approach to provide an effective solution for tracking multiple single-pixel nonmaneuvering and maneuvering targets. Through Monte Carlo simulations, we establish the efficiency and robustness of the proposed approach. Based on exhaustive empirical study, we demonstrate the effectiveness of our proposed approach in tracking multiple single-pixel targets in a sequence of infrared images with clutter and occlusion due to moving clouds in airborne applications. 相似文献
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A new modeling and filtering approach for tracking maneuvering targets is presented in thispaper.The approach,which makes optimal estimate for the model With the random variable possible,depends on random step modeling of target maneuvers.In the new model,the unknown targetacceleration is treated as a random variable and then estimated directly.A detector is designed tofind out the target maneuvers and the estimation algorithm will be restarted when the maneuvers oc-cur.Combination of three-dimention Kalman filter with a detector forms a tracker for maneuveringtargets.The new tracking scheme is easy to implement and its capability is illustrated in two trackingexamples in which the new approach is compared with Mooses'on the performance. 相似文献
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针对多编队机动目标先后出现时的跟踪问题,该文提出了一种基于交互式多模型高斯混合概率假设密度滤波(IMM-GM-PHD)算法的无先验信息跟踪方法。首先,在IMM-GM-PHD算法预测过程完成的基础上,引入密度检测机制,利用相关域为所有预测高斯分量挑选有效量测,结合密度聚类(DBSCAN)算法检测是否出现新编队目标。其次,在IMM-GM-PHD算法状态更新完成的基础上,利用更新高斯分量的组成情况完成模型概率的更新。最后,在状态估计优化过程中,结合编队目标的特点,加入相似度判别技术,利用杰森-香农(JS)散度度量高斯分量间的相似度,剔除没有相似分量的高斯分量,进一步优化估计结果。仿真结果表明,该文方法能够快速有效地跟踪非同时出现的多编队机动目标,具有较好的跟踪性能。 相似文献
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Traditionally, in target tracking, much emphasis is put on the motion model that realistically represents the target's movements. We first present the classical constant velocity model and then introduce a new model that incorporates an acceleration component along the heading direction of the target. We also show that the target motion parameters can be considered part of a more general feature set for target tracking. This is exemplified by showing that target frequencies, which may be unrelated to the target motion, can also be used to improve the tracking performance. In order to include the frequency variable, a new array steering vector is presented for the direction-of-arrival (DOA) estimation problems. The independent partition particle filter (IPPF) is used to compare the performances of the two motion models by tracking multiple maneuvering targets using the acoustic sensor outputs directly. The treatment is quite general since IPPF allows general type of noise models as opposed to Gaussianity imposed by Kalman type of formulations. It is shown that by incorporating the acceleration into the state vector, the tracking performance can be improved in certain cases as expected. Then, we demonstrate a case in which the frequency variable improves the tracking and classification performance for targets with close DOA tracks. 相似文献
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An important problem in surveillance and reconnaissance systems is the tracking of multiple moving targets in cluttered noise environments using outputs from a number of sensors possessing wide variations in individual characteristics and accuracies. A number of approaches have been proposed for this multitarget/multisensor tracking problem ranging from reasonably simple, though ad hoc, schemes to fairly complex, but theoretically optimum, approaches. In this paper, we describe an iterative procedure for time-recursive multitarget/multisensor tracking based on use of the expectation-maximization (EM) algorithm. More specifically, we pose the multitarget/multisensor tracking problem as an incomplete data problem with the observable sensor outputs representing the incomplete data, whereas the target-associated sensor outputs constitute the complete data. Target updates at each time use an EM-based approach that calculates the maximum a posteriori (MAP) estimate of the target states, under the assumption of appropriate motion models, based on the outputs of disparate sensors. The approach uses a Markov random field (MRF) model of the associations between observations and targets and allows for estimation of joint association probabilities without explicit enumeration. The advantage of this EM-based approach is that it provides a computationally efficient means for approaching the performance offered by theoretically optimum approaches that use explicit enumeration of the joint association probabilities. We provide selected results illustrating the performance/complexity characteristics of this EM-based approach compared with competing schemes 相似文献
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基于PDA的群目标合并与分离方法研究 总被引:2,自引:0,他引:2
针对飞行编队形成的群目标航迹关联的特殊性,分析了照射质心的编队群目标成员合并与分离检验方法的优缺点,定义了群目标成员合并与分离检验涉及的3要素:位置、速度和航迹历史。在此基础上,提出了基于概率数据关联(PDA)的群目标成员合并与分离的检验方法。该方法利用群目标等效回波统计中心更新滤波器状态,将每一个雷达周期的量测与统计中心关联判别,从而减少了关联次数,提高了正确判别群目标成员合并与分离的概率。仿真结果证明了该方法的有效性。 相似文献
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机动目标通常不是做恒定的运动,其运动状态会随时间的变化而变化.这就使描述系统运动的状态方程是非线性的,而且系统参数会不断变化.传统的推广卡尔曼滤波适用于定系统定参数的情况,如果运用到机动目标跟踪上会导致误差增大甚至滤波发散.基于此,将强跟踪滤波运用到机动目标跟踪上.强跟踪滤波在卡尔曼滤波的基础上引入了多重渐消因子,使强跟踪滤波具有极强的跟踪能力和较好地鲁棒性,因此可以很好地解决变系统变参数的问题.通过仿真,将强跟踪滤波与UT-BLUE滤波方法和EKF滤波方法进行比较,结果表明了该滤波方法的有效性和优越性. 相似文献
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EM(Expectation-Maximization)作为一种迭代求解非完备数据条件下极大似然(后验)参数估计问题的方法,在目标跟踪领域主要应用于被动跟踪及实时性要求不高的目标环境.该文推广了L.A.Johnston的理论成果,推导得出了一种基于AECM(Alternative Expectation ConditionMaximization)方法的杂波环境下实时机动目标跟踪箅法,算法中后验模型概率与关联概率由隐马尔科夫模型滤波计算得到.仿真计算表明,所提算法跟踪精度与IMM-PDA性能相当,算法是有效的. 相似文献