| 引用本文: | 国强,任海宁,周凯,戚连刚.非线性量测下的机动多目标跟踪[J].哈尔滨工业大学学报,2024,56(5):64.DOI:10.11918/202210098 |
| GUO Qiang,REN Haining,ZHOU Kai,QI Liangang.Multiple maneuvering target tracking with nonlinear measurements[J].Journal of Harbin Institute of Technology,2024,56(5):64.DOI:10.11918/202210098 |
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| 非线性量测下的机动多目标跟踪 |
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国强1,2,任海宁1,2,周凯1,2,戚连刚1,2
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(1.哈尔滨工程大学 信息与通信工程学院,哈尔滨 150001; 2.先进船舶通信与信息技术工业和信息化部重点实验室(哈尔滨工程大学),哈尔滨 150001)
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| 摘要: |
| 为了解决非线性量测下机动多目标跟踪实时性差、跟踪误差大以及对杂波变化鲁棒性较差的问题,基于随机有限集理论,提出了一种采用量测转换和模糊算法改进的多模型δ-广义标签多伯努利滤波器。首先,推导了交互多模型的δ-GLMB滤波器,通过去相关无偏量测转换实现位置量测从极坐标系到笛卡尔坐标系的无偏转换,并通过预测值去除量测误差和其协方差的相关性造成的滤波估计偏差,实现了非线性场景下的机动多目标跟踪；然后,通过航迹和量测的关联新息以及目标的机动约束构建联合波门,降低了杂波量测的数量；最后引入改进的模糊算法,以目标的模型后验概率为输入,根据模型的分离程度自适应调节运动模型的过程噪声,增加滤波精度。研究结果表明:在杂波环境下,通过与CKF-JMS-δ-GLMB、CKF-IMM-δ-GLMB等非线性多模型滤波器对比,所提算法计算时间较小,且跟踪精度更高,鲁棒性强。所提算法避免了传统的非线性处理方式计算量较大的问题,并且具有较好的杂波抑制特性,提升了非线性量测下机动多目标跟踪的性能。 |
| 关键词: 非线性量测 机动多目标 δ-广义标签多伯努利滤波器 量测转换 交互多模型 模糊算法 |
| DOI：10.11918/202210098 |
| 分类号:TN953 |
| 文献标识码:A |
| 基金项目:国家重点研发计划(2018YFE0206500)；国家自然科学基金(62071140)；中央高校基本科研业务费专项(3072022QBZ0801) |
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| Multiple maneuvering target tracking with nonlinear measurements |
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GUO Qiang1,2,REN Haining1,2,ZHOU Kai1,2,QI Liangang1,2
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(1.College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China; 2.Key Laboratory of Advanced Ship Communication and Information Technology (Harbin Engineering University), Ministry of Industry and Information Technology, Harbin 150001, China)
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| Abstract: |
| In order to solve the problems of poor real-time performance, significant tracking errors and poor robustness to clutter changes with maneuvering multi-target tracking with nonlinear measurement, a multi-model δ-generalized labeled multi-Bernoulli (δ-GLMB) filter is proposed in light of the random finite set (RFS) theory, capitalizing on measurement transformation and a fuzzy algorithm. Initially, the interactive multiple model (IMM) δ-GLMB filter realizes the unbiased conversion of position measurement from polar coordinate system to Cartesian coordinate system by initiating an uncorrelated unbiased measurement conversion and removes the filter estimation deviation caused by the correlation between measurement errors and their covariances on the basis of the predicted value, thus realizing the maneuvering multi-target tracking in nonlinear scenarios. Then, the number of clutter measurements is reduced by constructing a joint gate that takes into account track and measurement correlation and target maneuver constraints. Finally, an improved fuzzy algorithm is introduced, which takes the posterior model probability of the target as the input, to adaptively adjust the process noise of the motion model according to the separation degree of the model, thereby increasing filtering accuracy. The research result shows that in the clutter environment, compared with CKF-JMS-δ-GLMB, CKF-IMM-δ-GLMB, and other nonlinear filters, the proposed algorithm performs better in terms of computation time, tracking accuracy and robustness. The proposed algorithm sidesteps the computational burden typically associated with traditional nonlinear processing methods, and has better clutter suppression characteristics, which improves the performance of maneuvering multi-target tracking with nonlinear measurement. |
| Key words: nonlinear measurements maneuvering multiple target δ-generalized labeled multi-Bernoulli(δ-GLMB) converted measurement interactive multiple model fuzzy algorithm |
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