研究了多传感器多目标状态信息融合问题。
Multisensor multitarget state information fusion is studied in this paper.
研究了主被动多传感器多目标状态信息融合问题。
The problem of active passive multisensor multitarget state information fusion is studied.
研究炮兵系统多传感器多目标定位和跟踪的建模方法。
A method for the multi target locating and tracking with the multi sensor in a field artillery system is studied.
研究炮兵系统多传感器多目标定位和跟踪的建模方法。
Considering properties of targets and sensors used to detect targets, this paper addresses the multi sensor multi target locating and tracking in the artillery system.
基于因素空间理论，建立一种多传感器多目标识别方法。
A multisensor decision fusion method was created based on theory of factor Spaces.
多传感器多目标跟踪融合中的目标航迹包含了模糊信息，这种模糊信息可以用模糊隶属度函数来描述。
The target track in the multisensor multitarget tracking fusion contains the fuzzy information which was described by the fuzzy membership function.
研究了漏检情况下多传感器多目标检测中的数据关联问题，并将其描述为数学规划中组合最优化问题。
This paper studies the problem of data association in multisensor multitarget detection under the condition of leakage and describes it as combinatorial optimization in mathematical programming.
针对炮兵打击目标的特性和获取目标信息所采用的侦察设施，研究了多传感器多目标定位和跟踪问题。
Considering properties of targets and sensors used to detect targets, this paper addresses the multi sensor multi target locating and tracking in the artillery system.
该算法降低了多传感器多目标跟踪的复杂性和计算量，有效地解决了异类多传感器可用公共信息少的问题。
The presented algorithm can reduce computing complexity and solve the common information-lacking problem of heterogeneous sensors efficiently.
其次，对于多传感器多目标跟踪问题，数据关联是其中一项重要问题，也是实现多传感器数据融合的前提。
Secondly, for multi-sensor multi-target tracking, data association is one of important problems, and is the precondition of data fusion.
针对多传感器多目标航迹关联的特点，提出了将基于聚类分析的ISODATA算法应用于航迹关联的解决方法。
A method of track association based on ISODATA algorithm for clustering analysis is put forward according to the features of multi sensor multi target track association.
还提出了一种适合于实际工程应用的时空联合数据概率关摘要联算法，该算法解决了无源多传感器多目标跟踪的难题。
A time and space joint probabilistic data association algorithm is developed to solve the difficult problem of passive multisensor-multitarget tracking.
在虚警和漏检、密集目标环境下，该算法应用于多传感器多目标融合系统仿真，结果表明所述算法在多目标数据关联中有较好的可行性和优越性。
Under false alarm, miss detection and dense targets environment, this method is used in multisensor multitarget fusion system, and the result testifies that it can solve the association problems.
传感器管理的目的之一是将多传感器资源分配给多目标。
One of the sensor management purposes is to allocate sensors to targets.
基于概率统计模型给出了一种多传感器对多目标检测与分类的优化算法。
An optimizing algorithm of detection and classification used in multisensor and multitarget is put forward based on probability statistical model.
传统的最邻近联合概率数据关联算法（NNJPDA）不能直接用于多传感器对多目标的跟踪。
The Nearest Near Joint Probabilistic Data Association(NNJPDA) is not used directly in multi-sensor multi-target tracking.
基于信息增量提出了一种多传感器对多目标检测与分类的优化算法。
An optimizing algorithm based on information gain is put forward for multisensor detection and classification of multitargets.
在分布式多传感多目标信息融合系统中，由于每个局部传感器的采样频率不同以及具有不同的通信延迟，导致送入融合中心的局部航迹往往不是同步的。
In the distributed multi-sensor multi-target information fusion system, the local sensors usually provide the respective tracks at the different rates with different communication delays.
多目标杂波环境下，多传感器探测形成的航迹之间呈现错综复杂的关系，多传感器系统融合中心航迹管理成了迫切需要解决的问题。
For multiple targets in clutter, there is a complex connection among the tracks from multiple sensors. It is track management in fusion center with multiple sensors that becomes a key problem.
对于分布式多传感器融合多目标跟踪系统，提出一种序贯处理的航迹关联融合算法。
For distributed multi-sensor fusion multi-target tracking system, an efficient sequential track-to-track correlation and fusion algorithm is proposed.
对于分布式多传感器融合多目标跟踪系统，提出一种序贯处理的航迹关联融合算法。
For distributed multi-sensor fusion multi-target tracking system, an efficient sequential track-to-track correlation and fusion algorithm is proposed.
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