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首页> 外文会议>Conference on Infrared Materials, Devices, and Applications; 20071112-15; Beijing(CN) >IMMPDA algorithm for infrared target tracking based on multifeature fusion
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IMMPDA algorithm for infrared target tracking based on multifeature fusion

机译：基于多特征融合的IMMPDA红外目标跟踪算法

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摘要

The interacting multiple model probability data association (IMMPDA) algorithm is widely used to target tracking in clutter. However, it is difficult for IMMPDA to get high precision track when measurements of kinematics state is inaccurate, because it only considers kinematics feature of targets. To overcome the disadvantage, this paper presents an IMMPDA algorithm based on multi-feature fusion that utilizes multiple features of infrared targets such as kinematics state, size and gray. Association probabilities for targets position are calculated based on IMMPDA algorithm in the polar coordinates. Then the statistic distances of the size and gray are calculated according to state predictions and measurements. After that, statistic distances are further used to compute related association probabilities of targets that are in the validation region. The decision of synthetic data association of all targets in the validation region is made based on the information fusion, which uses fuzzy logic to get different weights of each feature. Experiments indicate that the proposed algorithm has high quality tracking performance. Compared with conventional IMMPDA algorithm, the new algorithm cannot only get higher accurate target association but also improve the stability of the infrared target tracking system.
机译：交互多模型概率数据关联（IMMPDA）算法被广泛用于杂波中的目标跟踪。但是，当运动状态的测量不准确时，IMMPDA很难获得高精度的跟踪，因为它仅考虑目标的运动特征。为了克服该缺点，本文提出了一种基于多特征融合的IMMPDA算法，该算法利用了红外目标的多种特征，如运动状态，大小和灰度。基于IMMPDA算法在极坐标中计算目标位置的关联概率。然后根据状态预测和测量结果计算尺寸和灰度的统计距离。此后，进一步使用统计距离来计算验证区域中目标的相关关联概率。基于信息融合确定验证区域中所有目标的综合数据关联，该信息融合使用模糊逻辑获得每个特征的不同权重。实验表明，该算法具有良好的跟踪性能。与传统的IMMPDA算法相比，新算法不仅获得了更高的精确目标关联性，而且提高了红外目标跟踪系统的稳定性。

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