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New track correlation algorithms in a multisensor data fusion system

机译：多传感器数据融合系统中的新轨迹相关算法

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摘要

In order to resolve the problem of track-to-track association in a distributed multisensor situation, this paper presents independent and dependent sequential track correlation algorithms based on Singer's and Bar-Shalom's algorithms. Based on sequential track correlation algorithm, the restricted and attenuation memory track correlation algorithms and sequential classic assignment rules are proposed. In this paper, these algorithms are described in detail. Then, the track correlation mass and multivalency processing methods are discussed as well. Finally, simulations are designed to compare the correlation performance of these algorithms with that of Singer's and Bar-Shalom's algorithms. The simulation results show that the performance of these algorithms proposed here is much better than that of the classical methods under the environments of dense targets, interfering, noise, track cross, and so on. Under the above situations, their correct correlation ratio is improved about 69 percent over the classical methods
机译：为了解决分布式多传感器情况下的轨道间关联问题，本文提出了基于Singer算法和Bar-Shalom算法的独立且相依的顺序轨道相关算法。在顺序轨迹相关算法的基础上，提出了受限和衰减记忆轨迹相关算法以及经典顺序分配规则。在本文中，将详细描述这些算法。然后，讨论了轨道相关质量和多价处理方法。最后，设计仿真以比较这些算法与Singer和Bar-Shalom算法的相关性能。仿真结果表明，在密集目标，干扰，噪声，磁道交叉等情况下，本文提出的算法性能优于经典方法。在上述情况下，它们的正确相关率比经典方法提高了约69％。

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