To improve the precision of multisensor data fusion, data or track association becomes the key of target tracking filter.
为了提高多传感器数据融合的精度，数据或航迹关联成为对目标跟踪滤波的关键。
The improved particle filter tracking algorithm not only keeps the high efficient operation, but also improves stability of target tracking.
改进后的粒子滤波跟踪算法不但保持了较高的运算效率，而且还较好地提高了跟踪的稳定性。
For the measurement noise problem, this note proposes a real time filter based on frequency analysis, which is capable of tracking large-scale maneuvering target.
针对测量数据的噪声，提出了一种基于频率分析的实时滤波方法，能够有效地跟踪大范阎机动目标的运动。
Extended Kalman Filter (EKF) and converted measurement Kalman Filter (CMKF) have been widely used in radar target tracking.
在雷达目标跟踪中，扩展卡尔曼滤波(ekf)和转换坐标卡尔曼滤波(CMKF)得到了广泛的应用。
The maneuvering target tracking(MTT) method includes variable dimension filter, input estimation and IMM, etc.
机动目标跟踪方法主要有变维滤波、输入估计方法和交互式多模方法。
When using Kalman Filter to track a target, estimation of the initial state of the target is an important factor influencing tracking precision in the initial phase.
采用卡尔曼滤波器对目标进行跟踪时，目标初始状态估计是影响初始阶段跟踪精度的一个重要原因。
Concerning the problem to instability and low accuracy of the passive filter on bearings-only target tracking, a modified adaptive Extended Kalman filter algorithm on polar coordinate is presented.
在水下被动目标跟踪系统中，直角坐标系下的扩展卡尔曼滤波器容易发散而导致滤波精度很差。
Because of the taking into consideration of the real time acceleration estimation, this filter method is applicable to maneuverable tracking of the target using its trajectory data.
由于考虑了加速度的实时估计，该滤波方法尤其适用于对弹道式目标的机动跟踪。
The results show that the tracking accuracy, the responsibility to target mobility and the implementation of steady Kalman filter are good.
结果表明：恒定增益卡尔曼滤波器在跟踪精度，对目标机动的响应能力以及可实现性等指标上都具有较好的特性。
The results of simulation indicate that this new approach has a better accuracy than the traditional Kalman filter and strong tracking Kalman filter in the field of maneuvering target tracking.
仿真结果表明，该改进滤波器跟踪机动目标的精度高于常规卡尔曼滤波器和强跟踪卡尔曼滤波器。
This method can reduce the weight degradation rate of particle aggregation which occurs in the particle filter moving target tracking method after a certain number of iterations.
该方法能降低基于粒子滤波的运动目标跟踪方法在迭代一定次数后会出现粒子权重聚集的粒子退化速度。
Kalman has already replaced the classic Wiener filter theory for it can not meet the development of modern target tracking system.
目前卡尔曼滤波理论基本上取代了维纳滤波理论，经典的维纳滤波方法基本上不适应现代目标跟踪系统的需求。
Tactical information of the certain air target can be used to improve the performance of the tracking filter.
可以利用战术背景下做战术机动空中目标的特性来提高跟踪效果。
In chapter two, the application of particle filter in tracking target is investigated.
第二章介绍粒子滤波在目标跟踪中的应用。
In MMW radar tracking, the classical Kalman filter will degrade seriously when observation noise is non-Gaussian because of target glint.
在毫米波雷达目标跟踪中，角闪烁的非高斯特性将使得经典的卡尔曼滤波器失效。
Then, it implements the data association and continuous tracking on the target candidates by using the particle filter and the FCM algorithm.
然后利用基于粒子滤波和FCM的算法实现对候选目标的数据关联和连续跟踪。
In this paper, research on particle filter algorithm for ballistic target tracking is carried on under the main background of nonlinearity, non-Gaussian noise.
本论文以非线性、非高斯噪声环境下的目标跟踪为主要背景，研究弹道导弹目标粒子滤波算法。
The Kalman filter has been commonly used in target tracking, however its performance may be degraded in presence of maneuver, low robustness and strong model dependence.
针对卡尔曼滤波器对系统模型依赖性强、鲁棒性差和跟踪机动目标能力有限的问题，提出了一种新的利用混合模糊逻辑和标准卡尔曼滤波器的联合算法。
Adaptive Kalman filter algorithm study (1) Based on the study of the passive target tracking in modified polar coordinates, the nonlinear dynamic model is devised.
自适应滤波算法(1)研究了极坐标系下的水下目标被动跟踪问题，建立了被动跟踪的动力学模型。
Concerning the problem of instability and low accuracy of passive filter in underwater target tracking, a modified adaptive extended kalman filter (MAEKF) algorithm is presented.
针对在被动方式下进行水下目标跟踪容易导致滤波发散和收敛精度不高的问题，介绍了一种改进的自适应推广卡尔曼滤波算法。
With a modified polar coordinate, a specific method to realize bearings-only target tracking by means of an extended Kalman filter is discussed in this paper.
本文讨论在修正极坐标下利用推广卡尔曼滤波实现纯方位目标跟踪的具体方法。
The conventional bearing only measurement tracking filter is unable to track a maneuvering target.
传统的仅有角测量跟踪器不能跟踪机动目标。
One maneuvering target tracking method in which RBF neural network is applied to correct Kalman filter result in standard Interacting Multi Model (IMM) is proposed in this paper.
文中提出了一种应用rbf神经网络对标准IMM算法中的卡尔曼滤波结果进行校正的方法。
In this paper, a brand new multi-target trajectory tracking algorithm based on random finite set theory is brought forward by adopting classical signal detection technique along with GMPHD filter.
在该文中，各目标的航迹信息以假设形式表述，数据互联则是通过使用经典的多元假设检测方法判决假设矩阵实现。
The state estimations algorithm for Target tracking have been studied and compared such as Kalman filter, Extented Kalman filter and Unscented Kalman filter.
对经典的卡尔曼滤波以及针对非线性系统的扩展卡尔曼滤波，不敏卡尔曼滤波算法进行了分析比较。
This paper presents a particle filter-based algorithm for IR target-tracking.
提出一种基于粒子滤波的红外目标跟踪的新算法。
Particle filter is an effective way of object tracking; however, it is still error-prone when it is used for multi-target tracking, especially when multiple objects overlapped.
粒子滤波是进行目标跟踪的有效方法，但被用于多目标跟踪时也容易出错，尤其是当多个目标相互交叉的时候。
Aiming at multisensor fusion based target tracking applications in wireless sensor networks, a mixed algorithm is proposed, called extended-mixed particle filter (EM-PF).
针对无线传感器网络中的多传感器融合目标跟踪，提出一种混合滤波算法，称为扩展混合粒子滤波算法(EM - PF)。
Single-radar target correlation and tracking filter research: In tracks correlation, it is most important that design a correct correlation gate size and paring algorithm for observation and tracks.
对于航迹相关算法给出了一种比较实用的单雷达航迹和系统航迹相关的算法，该算法消除了实际应用中出现的航迹相关错误。
Single-radar target correlation and tracking filter research: In tracks correlation, it is most important that design a correct correlation gate size and paring algorithm for observation and tracks.
对于航迹相关算法给出了一种比较实用的单雷达航迹和系统航迹相关的算法，该算法消除了实际应用中出现的航迹相关错误。
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