Kalman-Gain Aided Particle PHD Filter for Multitarget Tracking

Abdullahi Daniyan; Yu Gong; Sangarapillai Lambotharan; Pengming Feng; Jonathon Chambers

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Kalman-Gain Aided Particle PHD Filter for Multitarget Tracking

机译：用于多目标跟踪的卡尔曼增益辅助粒子PHD滤波器

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摘要

We propose an efficient sequential Monte Carlo probability hypothesis density (PHD) filter which employs the Kalman-gain approach during weight update to correct predicted particle states by minimizing the mean square error between the estimated measurement and the actual measurement received at a given time in order to arrive at a more accurate posterior. This technique identifies and selects those particles belonging to a particular target from a given PHD for state correction during weight computation. Besides the improved tracking accuracy, fewer particles are required in the proposed approach. Simulation results confirm the improved tracking performance when evaluated with different measures.

机译：我们提出了一种有效的顺序蒙特卡洛概率假设密度（PHD）滤波器，该滤波器在权重更新期间采用卡尔曼增益方法，通过最小化在给定时间接收的估计测量值与实际测量值之间的均方误差来校正预测的粒子状态。以获得更准确的后验。该技术从给定的PHD中识别并选择属于特定目标的那些粒子，以便在权重计算期间进行状态校正。除了提高跟踪精度外，该方法还需要更少的粒子。仿真结果证实了采用不同措施进行评估时跟踪性能的提高。

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来源
《IEEE Transactions on Aerospace and Electronic Systems》 |2017年第5期|2251-2265|共15页
作者
Abdullahi Daniyan; Yu Gong; Sangarapillai Lambotharan; Pengming Feng; Jonathon Chambers;
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作者单位

Loughborough University, Loughborough, U.K.;

Loughborough University, Loughborough, U.K.;

Loughborough University, Loughborough, U.K.;

Newcastle University, Newcastle upon Tyne, U.K.;

Newcastle University, Newcastle upon Tyne, U.K.;

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正文语种 eng
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Atmospheric measurements; Particle measurements; Time measurement; Proposals; Monte Carlo methods; Target tracking; Density functional theory;

机译：大气测量;颗粒测量;时间测量;建议;蒙特卡洛方法;目标跟踪;密度泛函理论;

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专利

1. An Efficient Data-Driven Particle PHD Filter for Multitarget Tracking [J] . Zheng Y., Shi Z., Lu R., IEEE transactions on industrial informatics . 2013,第4期

机译：用于多目标跟踪的高效数据驱动粒子PHD滤波器
2. Improving Multitarget Tracking in Synthetic Aperture Radar Within a High-Density Clutter, Using a Technique Based on the Promoted PHD Filtering and MTI Filter [J] . Daryasafar N., Sadeghzadeh R. A., Naser-Moghadasi M. Radio Science . 2018,第7a8期

机译：使用基于改进的PHD滤波和MTI滤波的技术改进高密度杂波中合成孔径雷达的多目标跟踪
3. Cardinality-Consensus-Based PHD Filtering for Distributed Multitarget Tracking [J] . Tiancheng Li, Franz Hlawatsch, Petar M. Djurić IEEE signal processing letters . 2019,第1期

机译：基于基数共识的PHD滤波用于分布式多目标跟踪
4. Multitarget PHD Particle Filter Tracker Based on Single-Target PHD [C] . Lingling Zhao, Xiaohong Su, Peijun Ma International Conference in Electrics, Communication and Automatic Control;ECAC2011 . 2012

机译：基于单目标PHD的多目标PHD粒子滤波跟踪器
5. Nonlinear filtering algorithms for multitarget tracking. [D] . Punithakumar, K. 2008

机译：用于多目标跟踪的非线性滤波算法。
6. A Survey of Recent Advances in Particle Filters and Remaining Challenges for Multitarget Tracking [O] . Xuedong Wang, Tiancheng Li, Shudong Sun, 2017

机译：粒子过滤器的最新研究概况以及多目标跟踪的剩余挑战
7. Kalman-gain aided particle PHD filter for multi-target tracking [O] . Daniyan Abdullah, Gong Yu, Lambotharan Sangarapillai, 2017

机译：卡尔曼增益辅助粒子PHD滤波器用于多目标跟踪
8. Theoretical Foundation for the Stein-Winter 'Probability Hypothesis Density (PHD)' Multitarget Tracking Approach. [R] . Mahler, R. P. 2000

机译：stein-Winter'概率假设密度（pHD）'多目标跟踪方法的理论基础。

Kalman-Gain Aided Particle PHD Filter for Multitarget Tracking

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