Abstract

In this work, a waveform agile sensing method for the target tracking problem has been described. By dynamically selecting the transmitted waveform from a generalized FM chirp signal library with different phase functions and chirp parameters, the tracker can improve the performance dramatically. The waveform design algorithm is based on the myopic optimization of a cost function, which is the predicted mean squared error. The cost function is approximated using the CRLB combined with unscented transformation. Then the generalized FM phase function and the corresponding parameters are configured according to a grid search over all the phase function candidates and allowable parameter values. Due to the nonlinearity of the measurement model in the tracking system, particle filtering is applied to track the state of the target. This approach was demonstrated by tracking a target moving in a 2-D cluttered environment with two active sensors. The simulation results showed that the mean squared error of the tracking was dramatically reduced by adaptively adjusting the transmitted signal.

Original languageEnglish (US)
Title of host publicationPrinciples of Waveform Diversity and Design
PublisherInstitution of Engineering and Technology
Pages394-418
Number of pages25
ISBN (Electronic)9781613531501
ISBN (Print)9781891121951
DOIs
StatePublished - Jan 1 2011

Fingerprint

Cost functions
Sensors
Target tracking

Keywords

  • 2D cluttered environment
  • Active sensors
  • Chirp modulation
  • Chirp parameters
  • Cost function
  • Cost function approximation
  • CRLB
  • Dynamically configured sensor systems
  • Generalized fm chirp signal library
  • Generalized fm phase function
  • Mean square error methods
  • Mean squared error
  • Mean squared error prediction
  • Measurement model
  • Measurement systems
  • Moving target tracking
  • Myopic optimization-based waveform design algorithm
  • Optimisation
  • Particle filtering
  • Particle filtering (numerical methods)
  • Radar clutter
  • Radar signal processing
  • Sensors
  • Signal transmission
  • Target tracking
  • Target tracking problem
  • Time-frequency analysis
  • Unscented transformation
  • Waveform agile sensing method
  • Waveform time-frequency characterization
  • Waveform transmission

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Li, Y., Sira, S. P., Papandreou-Suppappola, A., & Morrell, D. (2011). Waveform time-frequency characterization for dynamically configured sensor systems. In Principles of Waveform Diversity and Design (pp. 394-418). Institution of Engineering and Technology. https://doi.org/10.1049/SBRA023E_ch21

Waveform time-frequency characterization for dynamically configured sensor systems. / Li, Y.; Sira, S. P.; Papandreou-Suppappola, Antonia; Morrell, Darryl.

Principles of Waveform Diversity and Design. Institution of Engineering and Technology, 2011. p. 394-418.

Research output: Chapter in Book/Report/Conference proceedingChapter

Li, Y, Sira, SP, Papandreou-Suppappola, A & Morrell, D 2011, Waveform time-frequency characterization for dynamically configured sensor systems. in Principles of Waveform Diversity and Design. Institution of Engineering and Technology, pp. 394-418. https://doi.org/10.1049/SBRA023E_ch21
Li Y, Sira SP, Papandreou-Suppappola A, Morrell D. Waveform time-frequency characterization for dynamically configured sensor systems. In Principles of Waveform Diversity and Design. Institution of Engineering and Technology. 2011. p. 394-418 https://doi.org/10.1049/SBRA023E_ch21
Li, Y. ; Sira, S. P. ; Papandreou-Suppappola, Antonia ; Morrell, Darryl. / Waveform time-frequency characterization for dynamically configured sensor systems. Principles of Waveform Diversity and Design. Institution of Engineering and Technology, 2011. pp. 394-418
@inbook{949708195a204c3581911881c7e81deb,
title = "Waveform time-frequency characterization for dynamically configured sensor systems",
abstract = "In this work, a waveform agile sensing method for the target tracking problem has been described. By dynamically selecting the transmitted waveform from a generalized FM chirp signal library with different phase functions and chirp parameters, the tracker can improve the performance dramatically. The waveform design algorithm is based on the myopic optimization of a cost function, which is the predicted mean squared error. The cost function is approximated using the CRLB combined with unscented transformation. Then the generalized FM phase function and the corresponding parameters are configured according to a grid search over all the phase function candidates and allowable parameter values. Due to the nonlinearity of the measurement model in the tracking system, particle filtering is applied to track the state of the target. This approach was demonstrated by tracking a target moving in a 2-D cluttered environment with two active sensors. The simulation results showed that the mean squared error of the tracking was dramatically reduced by adaptively adjusting the transmitted signal.",
keywords = "2D cluttered environment, Active sensors, Chirp modulation, Chirp parameters, Cost function, Cost function approximation, CRLB, Dynamically configured sensor systems, Generalized fm chirp signal library, Generalized fm phase function, Mean square error methods, Mean squared error, Mean squared error prediction, Measurement model, Measurement systems, Moving target tracking, Myopic optimization-based waveform design algorithm, Optimisation, Particle filtering, Particle filtering (numerical methods), Radar clutter, Radar signal processing, Sensors, Signal transmission, Target tracking, Target tracking problem, Time-frequency analysis, Unscented transformation, Waveform agile sensing method, Waveform time-frequency characterization, Waveform transmission",
author = "Y. Li and Sira, {S. P.} and Antonia Papandreou-Suppappola and Darryl Morrell",
year = "2011",
month = "1",
day = "1",
doi = "10.1049/SBRA023E_ch21",
language = "English (US)",
isbn = "9781891121951",
pages = "394--418",
booktitle = "Principles of Waveform Diversity and Design",
publisher = "Institution of Engineering and Technology",
address = "United Kingdom",

}

TY - CHAP

T1 - Waveform time-frequency characterization for dynamically configured sensor systems

AU - Li, Y.

AU - Sira, S. P.

AU - Papandreou-Suppappola, Antonia

AU - Morrell, Darryl

PY - 2011/1/1

Y1 - 2011/1/1

N2 - In this work, a waveform agile sensing method for the target tracking problem has been described. By dynamically selecting the transmitted waveform from a generalized FM chirp signal library with different phase functions and chirp parameters, the tracker can improve the performance dramatically. The waveform design algorithm is based on the myopic optimization of a cost function, which is the predicted mean squared error. The cost function is approximated using the CRLB combined with unscented transformation. Then the generalized FM phase function and the corresponding parameters are configured according to a grid search over all the phase function candidates and allowable parameter values. Due to the nonlinearity of the measurement model in the tracking system, particle filtering is applied to track the state of the target. This approach was demonstrated by tracking a target moving in a 2-D cluttered environment with two active sensors. The simulation results showed that the mean squared error of the tracking was dramatically reduced by adaptively adjusting the transmitted signal.

AB - In this work, a waveform agile sensing method for the target tracking problem has been described. By dynamically selecting the transmitted waveform from a generalized FM chirp signal library with different phase functions and chirp parameters, the tracker can improve the performance dramatically. The waveform design algorithm is based on the myopic optimization of a cost function, which is the predicted mean squared error. The cost function is approximated using the CRLB combined with unscented transformation. Then the generalized FM phase function and the corresponding parameters are configured according to a grid search over all the phase function candidates and allowable parameter values. Due to the nonlinearity of the measurement model in the tracking system, particle filtering is applied to track the state of the target. This approach was demonstrated by tracking a target moving in a 2-D cluttered environment with two active sensors. The simulation results showed that the mean squared error of the tracking was dramatically reduced by adaptively adjusting the transmitted signal.

KW - 2D cluttered environment

KW - Active sensors

KW - Chirp modulation

KW - Chirp parameters

KW - Cost function

KW - Cost function approximation

KW - CRLB

KW - Dynamically configured sensor systems

KW - Generalized fm chirp signal library

KW - Generalized fm phase function

KW - Mean square error methods

KW - Mean squared error

KW - Mean squared error prediction

KW - Measurement model

KW - Measurement systems

KW - Moving target tracking

KW - Myopic optimization-based waveform design algorithm

KW - Optimisation

KW - Particle filtering

KW - Particle filtering (numerical methods)

KW - Radar clutter

KW - Radar signal processing

KW - Sensors

KW - Signal transmission

KW - Target tracking

KW - Target tracking problem

KW - Time-frequency analysis

KW - Unscented transformation

KW - Waveform agile sensing method

KW - Waveform time-frequency characterization

KW - Waveform transmission

UR - http://www.scopus.com/inward/record.url?scp=85013596004&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85013596004&partnerID=8YFLogxK

U2 - 10.1049/SBRA023E_ch21

DO - 10.1049/SBRA023E_ch21

M3 - Chapter

AN - SCOPUS:85013596004

SN - 9781891121951

SP - 394

EP - 418

BT - Principles of Waveform Diversity and Design

PB - Institution of Engineering and Technology

ER -