Multitarget detection using a real time joint transform correlator with power spectrum subtraction is proposed.
提出一种使用功率谱相减的实时联合变换相关器作多目标检测。
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.
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。
An optimizing algorithm of detection and classification used in multisensor and multitarget is put forward based on probability statistical model.
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。
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.
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.
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。
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