Joint Tracking and Classification of Extended Targets Using Random Matrix and Bernoulli Filter for Time-Varying Scenarios

Formula One races provide a wealth of data worth investigating. Although the time-varying data has a clear structure, it is pretty challenging to analyze it for further properties. Here the focus is on a visual classification for events, drivers, as well as time periods. As a first step, the Formula One data is visually encoded based on a line plot visual metaphor reflecting the dynamic lap times, and finally, a classification of the races based on the visual outcomes gained from these line plots is presented. The visualization tool is web-based and provides several interactively linked views on the data; however, it starts with a calendar-based overview representation. To illustrate the usefulness of the approach, the provided Formula One data from several years is visually explored while the races took place in different locations. The chapter discusses algorithmic, visual, and perceptual limitations that might occur during the visual classification of time-series data such as Formula One races.

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Joint Tracking of Manoeuvring Targets and Classification of Their Manoeuvrability

EURASIP Journal on Advances in Signal Processing ◽

10.1155/s1110865704404223 ◽

2004 ◽

Vol 2004 (15) ◽

Cited By ~ 17

Author(s):

Simon Maskell

Keyword(s):

Joint Tracking

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Reduction of Arbitrary Dynamical Systems by Wavelet Bases

Volume 6A: 18th Biennial Conference on Mechanical Vibration and Noise ◽

10.1115/detc2001/vib-21403 ◽

2001 ◽

Author(s):

Roger Ghanem ◽

Francesco Romeo

Keyword(s):

Dynamical Systems ◽

Reduction Process ◽

Inner Product ◽

Scaling Functions ◽

Time Varying ◽

Governing Equations ◽

Wavelet Bases ◽

Input And Output ◽

Analytical Expressions

Abstract A procedure is developed for the identification and classification of nonlinear and time-varying dynamical systems based on measurements of their input and output. The procedure consists of reducing the governing equations with respect to a basis of scaling functions. Given the localizing properties of wavelets, the reduced system is well adapted to predicting local changes in time as well as changes that are localized to particular components of the system. The reduction process relies on traditional Galerkin techniques and recent analytical expressions for evaluating the inner product between scaling functions and their derivatives. Examples from a variety of dynamical systems are used to demonstrate the scope and limitations of the proposed method.

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Joint Tracking and Classification of Multiple Targets with Scattering Center Model and CBMeMBer Filter

Sensors ◽

10.3390/s20061679 ◽

2020 ◽

Vol 20 (6) ◽

pp. 1679 ◽

Cited By ~ 1

Author(s):

Ronghui Zhan ◽

Liping Wang ◽

Jun Zhang

Keyword(s):

Sequential Monte Carlo ◽

Multiple Targets ◽

Range Resolution ◽

Aspect Angle ◽

Scattering Center ◽

Simulation Results ◽

Wideband Radar ◽

Joint Tracking ◽

High Range

This paper deals with joint tracking and classification (JTC) of multiple targets based on scattering center model (SCM) and wideband radar observations. We first introduce an SCM-based JTC method, where the SCM is used to generate the predicted high range resolution profile (HRRP) with the information of the target aspect angle, and target classification is implemented through the data correlation of observed HRRP with predicted HRRPs. To solve the problem of multi-target JTC in the presence of clutter and detection uncertainty, we then integrate the SCM-based JTC method into the CBMeMBer filter framework, and derive a novel SCM-JTC-CBMeMBer filter with Bayesian theory. To further tackle the complex integrals’ calculation involved in targets state and class estimation, we finally provide the sequential Monte Carlo (SMC) implementation of the proposed SCM-JTC-CBMeMBer filter. The effectiveness of the presented multi-target JTC method is validated by simulation results under the application scenario of maritime ship surveillance.

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Warning of Immortal Time Bias When Studying Drug Safety in Pregnancy: Application to Late Use of Antibiotics and Preterm Delivery

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17186465 ◽

2020 ◽

Vol 17 (18) ◽

pp. 6465

Author(s):

Giovanni Corrao ◽

Federico Rea ◽

Matteo Franchi ◽

Benedetta Beccalli ◽

Anna Locatelli ◽

...

Keyword(s):

Birth Weight ◽

Preterm Birth ◽

Low Birth Weight ◽

Preterm Delivery ◽

Apgar Score ◽

Time Varying ◽

Immortal Time Bias ◽

Increased Risk ◽

In Pregnancy

This study aimed to illustrate and account for immortal time bias in pregnancy observational investigations, using the relationship between late use of antibiotics and risk of preterm birth as an example. We conducted a population-based cohort study including 549,082 deliveries between 2007 and 2017 in Lombardy, Italy. We evaluated the risk of preterm births, low birth weight, small for gestational age, and low Apgar score associated with antibiotic dispensing during the third trimester of pregnancy. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CI) of the outcomes, considering the use of antibiotics as time-fixed (with biased classification of exposure person-time) and time-varying (with proper classification of exposure person-time) exposure. There were 23,638 (4.3%) premature deliveries. There was no association between time-fixed exposure to antibiotics and preterm delivery (adjusted HR 0.96; 95% CI 0.92 to 1.01) but an increased risk of preterm birth when time-varying exposure to antibiotics was considered (1.27; 1.21 to 1.34). The same trend was found for low birth weight and low Apgar score. Immortal time bias is a common and sneaky trap in observational studies involving exposure in late pregnancy. This bias could be easily avoided with suitable design and analysis.

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Tracking Multiple Marine Ships via Multiple Sensors with Unknown Backgrounds

Sensors ◽

10.3390/s19225025 ◽

2019 ◽

Vol 19 (22) ◽

pp. 5025 ◽

Cited By ~ 1

Author(s):

Cong-Thanh Do ◽

Tran Thien Dat Nguyen ◽

Weifeng Liu

Keyword(s):

State Of The Art ◽

Numerical Study ◽

Probability Of Detection ◽

Multitarget Tracking ◽

Time Varying ◽

Multiple Sensors ◽

Doppler Data ◽

Online Tracking ◽

Bernoulli Filter ◽

Parallel Estimation

In multitarget tracking, knowledge of the backgrounds plays a crucial role in the accuracy of the tracker. Clutter and detection probability are the two essential background parameters which are usually assumed to be known constants although they are, in fact, unknown and time varying. Incorrect values of these parameters lead to a degraded or biased performance of the tracking algorithms. This paper proposes a method for online tracking multiple targets using multiple sensors which jointly adapts to the unknown clutter rate and the probability of detection. An effective filter is developed from parallel estimation of these parameters and then feeding them into the state-of-the-art generalized labeled multi-Bernoulli filter. Provided that the fluctuation of these unknown backgrounds is slowly-varying in comparison to the rate of measurement-update data, the validity of the proposed method is demonstrated via numerical study using multistatic Doppler data.

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