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Cited by 11Target Tracking and Data Fusion in Sensor NetworksAnomaly Detection Techniques and ApplicationsRadar Systems and Signal Processing

The DOI matches its Crossref record.

Random Finite Set Tracking for Anomaly Detection in the Presence of Clutter

Forti, Nicola, Millefiori, Leonardo M., Braca, Paolo, Willett, Peter·2020·2020 IEEE Radar Conference (RadarConf20)
DOI 10.1109/radarconf2043947.2020.9266705
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Abstract

In this paper, a sequential Bayesian framework is proposed to address the task of joint anomaly detection and tracking for surveillance applications in the presence of clutter. This is achieved by modeling the anomaly as a switching unknown control input which goes into action by modifying the expected dynamics of a target and ceases its activity (becomes nonexistent) under nominal behavior. Random Finite Sets (RFS) make it possible to represent the switching nature of the object anomalous behavior and derive a hybrid Bernoulli filter (HBF) that sequentially updates the joint posterior density of a Bernoulli RFS for the unknown velocity input and the object kinematic state. In addition, the proposed HBF has been customized for maritime anomaly detection by using a piecewise Ornstein-Uhlenbeck (OU) stochastic process as dynamic model of vessels. We illustrate the effectiveness of the proposed filter, implemented in Gaussian-mixture form, and compare its performance in a maritime surveillance example with the Interacting Multiple Model Probabilistic Data Association Filter (IMM - PDAF) for different levels of clutter.

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Research works printed but not linked: 32

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Metadata

Authors
Forti, N., Millefiori, L. M., Braca, P., & Willett, P.
Affiliations
  • NATO Centre for Maritime Research and Experimentation (IT)
  • University of Connecticut (US)
ORCID
Venue
2020 IEEE Radar Conference (RadarConf20)
Publisher
IEEE
Published
2020-09-21
Pages
1-6
ISSN
1097-5659 (ISSN-L)
DOI
10.1109/radarconf2043947.2020.9266705
OpenAlex
W3112751475
Cited by
11
References
0
Citations by year
2025: 3 · 2024: 1 · 2023: 4 · 2021: 3
FWCI
0.67
Type
conference paper
Language
en
Open access
closed
Licence
Locations
  • IEEE Radar Conference (RadarConf) (publishedVersion)
  • CINECA IRIS Institutial research information system (University of Pisa) (submittedVersion)
  • Florence Research (University of Florence) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
  • Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
  • Physical Sciences › Engineering › Aerospace Engineering › Radar Systems and Signal Processing
Keywords
random finite sets, anomaly detection, target tracking, clutter, sequential Bayesian inference, maritime surveillance, vessel dynamics, Gaussian mixture model

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