多扩展对象的Poisson多重Bernoulli逼近 过滤

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摘要翻译：
Poisson多Bernoulli混合(PMBM)是闭式Bayes随机有限集滤波器的多目标共轭先验。扩展对象PMBM过滤器为使用标准模型的多个扩展对象过滤提供了一个封闭形式的解决方案。本文通过滤波递归传递一个Poisson多重Bernoulli(PMB)密度来考虑扩展对象PMBM滤波器的计算量更轻的替代方案。提出了一种新的局部假设表示，其中每个度量值都产生一个新的伯努利分量。这促进了在更新步骤之后有效地将PMBM后验密度近似为PMB的方法的发展。基于新的假设表示，提出了两种近似方法：一种是基于航迹定向的多Bernoulli(MB)近似，另一种是基于Kullback-Leibler散度最小化的变分MB近似。在一个仿真研究中，评估了所提出的PMB滤波器的性能与Gamma-Gaussian逆Wishart实现。
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英文标题：
《Poisson Multi-Bernoulli Approximations for Multiple Extended Object
  Filtering》
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作者：
Yuxuan Xia, Karl Granstr\"om, Lennart Svensson, Maryam Fatemi, \'Angel
  F. Garc\'ia-Fern\'andez, Jason L. Williams
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最新提交年份：
2021
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  The Poisson multi-Bernoulli mixture (PMBM) is a multi-object conjugate prior for the closed-form Bayes random finite sets filter. The extended object PMBM filter provides a closed-form solution for multiple extended object filtering with standard models. This paper considers computationally lighter alternatives to the extended object PMBM filter by propagating a Poisson multi-Bernoulli (PMB) density through the filtering recursion. A new local hypothesis representation is presented where each measurement creates a new Bernoulli component. This facilitates the developments of methods for efficiently approximating the PMBM posterior density after the update step as a PMB. Based on the new hypothesis representation, two approximation methods are presented: one is based on the track-oriented multi-Bernoulli (MB) approximation, and the other is based on the variational MB approximation via Kullback-Leibler divergence minimisation. The performance of the proposed PMB filters with gamma Gaussian inverse-Wishart implementations are evaluated in a simulation study.
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PDF链接：
https://arxiv.org/pdf/1801.01353

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关键词：Bernoulli poisson Nou Iss Ber 对象 封闭 表示 过滤 促进