杂波环境下雷达/红外数据融合目标跟踪算法的研究

Research on Radar/Infrared Data Fusion Target Tracking Algorithm in Cluttering

在杂波环境中,利用雷达/红外传感器跟踪机动目标的背景下.针对传统的概率数据关联理论在解决量测中多个目标回波以及数目等问题方面的不足,提出了一种基于交互式多模型和多传感器多检测概率数据关联的融合算法(IMM/MS-MDPDAF). IM M算法具有适应目标高机动和杂波环境的能力,M S-M DPDAF算法能够检测到多个有效目标回波,考虑了杂波环境下的多种不确定性.依据雷达传感器的多重检测模式对目标进行有效量测以及状态向量的更新,再通过数据融合和概率数据关联理论在贝叶斯框架下进行相应的概率计算和状态预测、估计和更新,从而实现对机动目标的连续估计.仿真结果表明,IMM/M S-M DPDAF算法能够提高目标跟踪的有效性和跟踪精度,具有比IM M/M SPDAF更好的跟踪性能.

In the clutter environment,radar/infrared sensors are used to track the background of maneuvering targets. Aiming at the shortcomings of traditional probabilistic data association theory in solving the problems of multiple target echoes and number in measurement,a fusion algorithm based on interactive multi-model and multi-sensor multi-detection probabilistic data association( IMM/MSM DPDAF) was proposed. The IMM algorithm has the ability to adapt to the target high maneuver and clutter environment,the MSM DPDAF algorithm can detect multiple effective target echoes,considering various uncertainties in the clutter environment. According to the multiple detection mode of radar sensor,the target is measured effectively and the status vector is updated,and the probability calculation and state prediction,estimation and update are carried out under Bayes framework by data fusion and probabilistic data correlation theory,thus realizing continuous estimation of the maneuvering target. The simulation results showthat the IMM/MDPDAF algorithm can improve the effectiveness and tracking accuracy of the target,and have better tracking performance than the IMM/MSPDAF.