Regional variance for multi-object filtering

Emmauel Delande; Murat Uney; Jeremie Houssineau; Daniel Clark

doi:10.1109/TSP.2014.2328326

Regional variance for multi-object filtering

Emmauel Delande (Lead Author), Murat Uney, Jeremie Houssineau, Daniel Clark

School of Engineering

Research output: Contribution to journal › Article › peer-review

Abstract / Description of output

Recent progress in multi-object filtering has led to algorithms that compute the first-order moment of multi-object distributions based on sensor measurements. The number of targets in arbitrarily selected regions can be estimated using the first-order moment. In this work, we introduce explicit formulae for the computation of the second-order statistic on the target number. The proposed concept of regional variance quantifies the level of confidence on target number estimates in arbitrary regions and facilitates information-based decisions. We provide algorithms for its computation for the probability hypothesis density (PHD) and the cardinalized probability hypothesis density (CPHD) filters. We demonstrate the behaviour of the regional statistics through simulation examples.

Original language	English
Pages (from-to)	3415 -- 3428
Number of pages	13
Journal	IEEE Transactions on Signal Processing
Volume	62
Issue number	13
Early online date	6 Jun 2014
DOIs	https://doi.org/10.1109/TSP.2014.2328326
Publication status	Published - 1 Jul 2014

Keywords / Materials (for Non-textual outputs)

Multi-object filtering
higher-order statistics
PHD filter
CPHD Filter
random finite sets
Bayesian estimation
Target tracking

Access to Document

10.1109/TSP.2014.2328326

http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6824846

Signal Processing in the Networked Battlespace
Mulgrew, B., Davies, M., Hopgood, J. & Thompson, J.
EPSRC
1/04/13 → 30/06/18
Project: Research
Sensor Signal Processing
Mulgrew, B.
Other
1/06/12 → 31/08/16
Project: Research

Cite this

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title = "Regional variance for multi-object filtering",

abstract = "Recent progress in multi-object filtering has led to algorithms that compute the first-order moment of multi-object distributions based on sensor measurements. The number of targets in arbitrarily selected regions can be estimated using the first-order moment. In this work, we introduce explicit formulae for the computation of the second-order statistic on the target number. The proposed concept of regional variance quantifies the level of confidence on target number estimates in arbitrary regions and facilitates information-based decisions. We provide algorithms for its computation for the probability hypothesis density (PHD) and the cardinalized probability hypothesis density (CPHD) filters. We demonstrate the behaviour of the regional statistics through simulation examples.",

keywords = "Multi-object filtering, higher-order statistics, PHD filter, CPHD Filter , random finite sets, Bayesian estimation, Target tracking",

author = "Emmauel Delande and Murat Uney and Jeremie Houssineau and Daniel Clark",

year = "2014",

month = jul,

day = "1",

doi = "10.1109/TSP.2014.2328326",

language = "English",

volume = "62",

pages = "3415 ---- 3428",

journal = "IEEE Transactions on Signal Processing",

issn = "1053-587X",

publisher = "Institute of Electrical and Electronics Engineers",

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T1 - Regional variance for multi-object filtering

AU - Delande, Emmauel

AU - Uney, Murat

AU - Houssineau, Jeremie

AU - Clark, Daniel

PY - 2014/7/1

Y1 - 2014/7/1

N2 - Recent progress in multi-object filtering has led to algorithms that compute the first-order moment of multi-object distributions based on sensor measurements. The number of targets in arbitrarily selected regions can be estimated using the first-order moment. In this work, we introduce explicit formulae for the computation of the second-order statistic on the target number. The proposed concept of regional variance quantifies the level of confidence on target number estimates in arbitrary regions and facilitates information-based decisions. We provide algorithms for its computation for the probability hypothesis density (PHD) and the cardinalized probability hypothesis density (CPHD) filters. We demonstrate the behaviour of the regional statistics through simulation examples.

AB - Recent progress in multi-object filtering has led to algorithms that compute the first-order moment of multi-object distributions based on sensor measurements. The number of targets in arbitrarily selected regions can be estimated using the first-order moment. In this work, we introduce explicit formulae for the computation of the second-order statistic on the target number. The proposed concept of regional variance quantifies the level of confidence on target number estimates in arbitrary regions and facilitates information-based decisions. We provide algorithms for its computation for the probability hypothesis density (PHD) and the cardinalized probability hypothesis density (CPHD) filters. We demonstrate the behaviour of the regional statistics through simulation examples.

KW - Multi-object filtering

KW - higher-order statistics

KW - PHD filter

KW - CPHD Filter

KW - random finite sets

KW - Bayesian estimation

KW - Target tracking

U2 - 10.1109/TSP.2014.2328326

DO - 10.1109/TSP.2014.2328326

M3 - Article

SN - 1053-587X

VL - 62

SP - 3415

EP - 3428

JO - IEEE Transactions on Signal Processing

JF - IEEE Transactions on Signal Processing

IS - 13

ER -

Regional variance for multi-object filtering

Abstract / Description of output

Keywords / Materials (for Non-textual outputs)

Access to Document

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Projects

Signal Processing in the Networked Battlespace

Sensor Signal Processing

Cite this