Generic Distributed Target Tracking Algorithms in Sensor Networks with Finite Set Statistics

具有有限集统计的传感器网络中的通用分布式目标跟踪算法

• 批准号: EP/H011900/1
• 负责人: Daniel Clark
• 金额: $ 20.61万
• 依托单位: Heriot-Watt University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2010
• 资助国家: 英国
• 起止时间: 2010 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FH011900%2F1
• 关键词: Generic; Distributed; Target; Tracking; Algorithms

This research programme will investigate and develop new distributed multi-target multi-source detection (DMMD) and tracking algorithms for sensor networks with constrained communication resources. Current approaches to DMMD have generalised distributed data fusion (DDF) algorithms by combining them with multiple hypothesis tracking (MHT) algorithms. However, the approximations inherent in MHT can lead to an unacceptable degradation in tracking performance. To overcome this difficulty, we propose to develop a new DMMD algorithm that builds upon Finite Set Statistics (FISST) and Exponential Mixture Densities (EMD). FISST provides a rigorous and numerical tractable model that unifies the problems of multi-object multi-sensor detection, classification and estimation. EMD is a suboptimal algorithm for fusing estimates when their marginal distributions are known but their joint distribution is not. It can be used to fuse estimates in fusion networks where the network topology is arbitrary, unknown and time varying.There will be two main outcomes from this research programme:First, we shall create an extremely general and generic mathematical framework within which a range of non-linear filtering algorithms can be deployed. Second, we shall develop implementations that, we believe, will show significant advantages over existing approaches in their ability to deal with high false alarm rates and data association ambiguity. We shall also strive for computational efficiency and practical applicability. The successful extension to distributed environments could have widespread applicability due to their simplicity to implement and low complexity.This programme is in response to the Detection requirement and Challenge Number 13 of the EPSRC-DSTL call: ``To develop general algorithms for distributed signal fusion in a network of sensors.''

这项研究计划将研究和开发新的分布式多目标多源检测(DMMD)和跟踪算法，用于通信资源受限的传感器网络。当前的DMMD方法通过将分布式数据融合(DDF)算法与多假设跟踪(MHT)算法相结合来推广这些算法。然而，MHT固有的近似可能会导致跟踪性能的不可接受的下降。为了克服这一困难，我们提出了一种新的基于有限集统计(FISST)和指数混合密度(EMD)的DMMD算法。FISST提供了一个严格的、数值易处理的模型，统一了多目标多传感器的检测、分类和估计问题。当估计的边缘分布已知而联合分布未知时，EMD是一种次优的融合估计算法。它可以用于融合网络中的估计，在网络拓扑任意、未知和时间变化的情况下。这项研究计划将产生两个主要结果：第一，我们将创建一个非常通用的数学框架，在其中可以部署一系列非线性滤波算法。其次，我们将开发实现，我们相信，在处理高虚警率和数据关联模糊性的能力方面，将显示出比现有方法显著的优势。我们还将努力提高计算效率和实用性。成功地扩展到分布式环境可能具有广泛的适用性，因为它们易于实现和低复杂性。该计划是为了响应EPSRC-DSTL呼叫的检测要求和挑战13：“为传感器网络中的分布式信号融合开发通用算法。”

项目成果

Robust multi-object sensor fusion with unknown correlations

• DOI: 10.1049/ic.2010.0233
• 发表时间: 2010
• 作者: Daniel E. Clark;S. Julier;R. Mahler;B. Ristic

Regional Variance for Multi-Object Filtering

• DOI: 10.1109/tsp.2014.2328326
• 发表时间: 2013-10
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: E. Delande;Murat Üney;J. Houssineau;Daniel E. Clark

Information measures in distributed multitarget tracking

分布式多目标跟踪中的信息度量

• 发表时间: 2011
• 期刊: Fusion 2011 - 14th International Conference on Information Fusion
• 作者: Uney M.

Monte Carlo realisation of a distributed multi-object fusion algorithm

分布式多目标融合算法的蒙特卡罗实现

• DOI: 10.1049/ic.2010.0232
• 发表时间: 2010
• 作者: Uney M

Distributed Fusion of PHD Filters Via Exponential Mixture Densities

• DOI: 10.1109/jstsp.2013.2257162
• 发表时间: 2013-06-01
• 期刊: IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING
• 影响因子: 7.5
• 作者: Ueney, Murat;Clark, Daniel E.;Julier, Simon J.
• 通讯作者: Julier, Simon J.