Robust Δ-Generalized Labeled Multi-Bernoulli Filter for Nonlinear Systems with Heavy-tailed Noises

机译：带有重尾噪声的非线性系统的鲁棒Δ广义标记多伯努利滤波器

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摘要

To solve the problem of multi-target tracking with heavy-tailed process noise and measurement noise, a Student's t mixture δ-generalized labeled multi-Bernoulli ( δ-GLMB) filter is proposed for nonlinear systems. A third-degree Spherical-Radial rule is utilized to calculate the probability density functions of the prediction and update of target states for nonlinear multi-target models. The performance of the proposed Student's t mixture δ-GLMB filter for nonlinear systems is compared with the Sequential Monte Carlo δ-GLMB (SMC- δ-GLMB) filter through simulation experiments. Simulation results demonstrated that the proposed filter can achieve a good trade-off between efficiency and tracking accuracy.

机译：为了解决带有重尾过程噪声和测量噪声的多目标跟踪问题，针对非线性系统，提出了一种学生t混合δ广义标记多伯努利（δ-GLMB）滤波器。三次球面-径向规则用于计算非线性多目标模型的目标状态预测和更新的概率密度函数。通过仿真实验，将所建议的学生t混合δ-GLMB滤波器用于非线性系统的性能与顺序蒙特卡洛δ-GLMB（SMC-δ-GLMB）滤波器进行了比较。仿真结果表明，所提出的滤波器可以在效率和跟踪精度之间取得良好的折衷。

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《International Conference on Information Fusion》|2020年|1-8|共8页
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Liming Hou; Feng Lian; Giuseppe Thadeu Freitas de Abreu; Shuncheng Tan;
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Target tracking; Noise measurement; Probability density function; Bayes methods; Extraterrestrial measurements; Gaussian distribution; Time measurement;

机译：目标跟踪噪声测量概率密度函数贝叶斯方法地外测量高斯分布时间测量;

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专利

1. Student-t mixture labeled multi-Bernoulli filter for multi-target tracking with heavy-tailed noise [J] . Dong Peng, Jing Zhongliang, Leung Henry, Signal processing . 2018,第NOVa期

机译：Student-t混合物标记的多伯努利滤波器，用于带有重尾噪声的多目标跟踪
2. Derivation of the multi-model generalized labeled multi-Bernoulli filter: a solution to multi-target hybrid systems [J] . Weihua WU, Yichao CAI, Hongbin JIN, 浙江大学学报（英文版）（C辑：计算机与电子） . 2021,第001期

机译：多模型广义标记多Bernoulli过滤器的推导：多目标混合系统的解决方案
3. Robust extended Kalman filtering for nonlinear stochastic systems with random sensor delays, packet dropouts and correlated noises [J] . Qian Huaming, Qiu Zhenbing, Wu Yonghui Aerospace science and technology . 2017,第Jula期

机译：具有随机传感器延迟，数据包丢失和相关噪声的非线性随机系统的鲁棒扩展卡尔曼滤波
4. A Heavy-Tailed Noise Tolerant Labeled Multi-Bernoulli Filter [C] . Wanying Zhang, Feng Yang, Yan Liang, International Conference on Information Fusion . 2018

机译：重尾噪声标记的多伯努利滤波器
5. Cumulant filters: Nonlinear filters for recursive state estimation of systems with non-Gaussian noise. [D] . Latimer, Jose R. 2004

机译：累积滤波器：非线性滤波器，用于对具有非高斯噪声的系统进行递归状态估计。
6. An Adaptive Filter for Nonlinear Multi-Sensor Systems with Heavy-Tailed Noise [O] . Xiangxiang Dong, Luigi Chisci, Yunze Cai 2020

机译：具有重尾噪声的非线性多传感器系统的自适应滤波器
7. Robust Generalized Labeled Multi-Bernoulli Filter for Multitarget Tracking with Unknown Non-stationary Heavy-tailed Measurement Noise [O] . Liming Hou, Feng Lian, Shuncheng Tan, 2021

机译：具有未知非静止重尾测量噪声的多级跟踪的鲁棒广泛标记的多Bernoulli滤波器

Robust Δ-Generalized Labeled Multi-Bernoulli Filter for Nonlinear Systems with Heavy-tailed Noises

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