Nonlinear Application Extension for δ-Generalized Labeled Multi-Bernoulli Filter

doi:10.7652/xjtuxb201906015

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Nonlinear Application Extension for δ-Generalized Labeled Multi-Bernoulli Filter

更新时间：2025-07-09

- Nonlinear Application Extension for δ-Generalized Labeled Multi-Bernoulli Filter
- Vol. 53, Issue 6, Pages: 109-116(2019)
- 作者机构：
  
  1. 西安交通大学智能网络与网络安全教育部重点实验室,西安,710049
  2. 陕西省组合与智能导航重点实验室,西安,710068
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201906015
  CLC： TP393
- Online First：10 June 2019，
  
  Published：2019
- 稿件说明：
移动端阅览
Nonlinear Application Extension for δ-Generalized Labeled Multi-Bernoulli Filter[J]. 2019, 53(6): 109-116.
DOI：

Nonlinear Application Extension for δ-Generalized Labeled Multi-Bernoulli Filter[J]. 2019, 53(6): 109-116. DOI： 10.7652/xjtuxb201906015.

摘要

针对非线性模型下δ-广义标记多伯努利(δ-GLMB)滤波器的序贯蒙特卡洛(SMC)实现过程计算复杂度过高、难以实现快速准确滤波的问题

给出了δ-GLMB滤波器的积分卡尔曼高斯混合(QK-GM)实现过程。该算法基于Gauss-Hermite数值积分规则获取一组带权重的积分点

利用这些积分点求取多目标密度函数的均值和协方差矩阵。将该算法与已有的扩展卡尔曼高斯混合(EK-GM)实现、无味卡尔曼高斯混合(UK-GM)实现和SMC实现在不同的杂波强度和检测概率条件下就多目标跟踪精度和时间消耗等方面做了较为详细的对比

结果表明

与SMC实现方法相比

QK-GM-δ-GLMB算法能以完全可接受的时间开销为代价

将多目标跟踪精度提高10%以上。该算法为δ-GLMB滤波器在非线性场景中的应用提供了一种新的实现方法。

Abstract

Quadrature Kalman Gaussian mixture(QK-GM)implementation for δ-generalized labeled multi-Bernoulli(δ-GLMB)filter is proposed to solve the problem that the sequential Monte Carlo(SMC)implementation for δ-GLMB filter in nonlinear model is too complex to realize fast and accurate filtering. The algorithm is based on the Gauss-Hermite quadrature rule to obtain a set of weighted quadrature points

and these points are then utilized to calculate mean values and covariance matrixes of the multiobjective density functions. The proposed QK-GM implementation is compared with the existing algorithms

including extended Kalman Gaussian mixture(EK-GM)

unscented Kalman Gaussian mixture(UK-GM)and SMC implementation

in detail in terms of tracking accuracy and time consumption for multi-target tracking in the presence of different clutter densities and detection probabilities. Simulation results and a comparison with SMC method show that the QK-GM implementation of δ-GLMB filter improves the multi-target tracking accuracy by more than 10% at the expense of a completely acceptable time cost. The proposed algorithm may provide a new method for applications of δ-GLMB filter in nonlinear scenes.

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references

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