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.''

该研究计划将调查并开发新的分布式多目标多源多源检测（DMMD），并针对传感器网络的跟踪算法，并具有限制的通信资源。当前的DMMD方法通过将它们与多个假设跟踪（MHT）算法相结合，具有通用的分布数据融合（DDF）算法。但是，MHT固有的近似值可能导致跟踪性能的不可接受的降解。为了克服这一困难，我们建议开发一种新的DMMD算法，该算法以有限的集合统计（FISST）和指数混合密度（EMD）为基础。 FISST提供了一个严格且数值的可拖动模型，该模型统一了多对象多传感器检测，分类和估计的问题。 EMD是一种次优算法，用于融合估算的估计值，但它们的联合分布却不是。它可用于融合融合网络中的估计值，其中网络拓扑是任意，未知且时间变化的。该研究程序将有两个主要结果：首先，我们将创建一个极其通用和通用的数学框架，其中一系列非线性过滤算法可以部署。其次，我们将开发实施情况，我们认为，在处理高误报率和数据关联歧义的能力方面，我们将在现有方法上显示出显着优势。我们还将努力提高计算效率和实际适用性。 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.''

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.