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首页> 外文会议>Conference on Signal and Data Processing of Small Targets 2002, Apr 2-4, 2002, Orlando, USA >Efficient Particle Filters for Joint Tracking and Classification
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Efficient Particle Filters for Joint Tracking and Classification

机译：用于联合跟踪和分类的高效粒子过滤器

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摘要

Target tracking is usually performed using data from sensors such as radar, whilst the target identification task usually relies on information from sensors such as IFF, ESM or imagery. The differing nature of the data from these sensors has generally led to these two vital tasks being performed separately. However, it is clear that an experienced operator can observe behaviour characteristics of targets and, in combination with knowledge and expectations of target type and likely activity, can more knowledgeably identify the target and robustly predict its track than any automatic process yet defined. Most trackers are designed to follow targets within a wide envelope of trajectories and are not designed to derive behaviour characteristics or include them as part of their output. Thus, there is potential scope for both applying target type knowledge to improve the reliability of the tracking process, and to derive behavioural characteristics which may enhance knowledge about target identity and/or activity. In this paper we introduce a Bayesian framework for joint tracking and identification and give a robust and computationally efficient particle filter based algorithm for numerical implementation of the resulting recursions. Simulation results illustrating algorithm performance are presented.
机译：通常使用来自雷达等传感器的数据执行目标跟踪，而目标识别任务通常依赖于来自IFF，ESM或图像等传感器的信息。来自这些传感器的数据的不同性质通常导致这两项重要任务被分别执行。但是，很显然，有经验的操作员可以观察目标的行为特征，并且与目标类型和可能的活动的知识和期望相结合，可以比尚未定义的任何自动过程更熟练地识别目标并可靠地预测其轨迹。大多数跟踪器的设计目标是在大范围的轨迹内跟踪目标，而不是旨在导出行为特征或将其包含为输出的一部分。因此，存在潜在的范围，既可以应用目标类型知识来提高跟踪过程的可靠性，又可以获取可以增强有关目标身份和/或活动知识的行为特征。在本文中，我们介绍了一种用于联合跟踪和识别的贝叶斯框架，并给出了基于健壮且计算效率高的基于粒子滤波的算法，用于对所得递归进行数值实现。给出了说明算法性能的仿真结果。

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