Online ISSN
1751-9683
Print ISSN
1751-9675
IET Signal Processing
Volume 4, Issue 4, August 2010
Volumes & issues:
Volume 4, Issue 4
August 2010
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- Author(s): T. Thayaparan ; L. Stankovic ; M. Amin ; V. Chen ; L. Cohen ; B. Boashash
- Source: IET Signal Processing, Volume 4, Issue 4, p. 325 –328
- DOI: 10.1049/iet-spr.2010.9095
- Type: Article
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- Author(s): W. Brinkman and T. Thayaparan
- Source: IET Signal Processing, Volume 4, Issue 4, p. 329 –342
- DOI: 10.1049/iet-spr.2009.0082
- Type: Article
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Algorithms based on the genetic algorithm (GA) and the particle swarm optimisation (PSO) algorithm were designed for focusing inverse synthetic aperture radar (ISAR) images that suffered from degradation because of Doppler smearing. These algorithms optimised the adaptive joint-time–frequency (AJTF) algorithm by replacing the exhaustive search as the primary search tool used to determine focusing parameters. The use of the PSO for ISAR image focusing is a unique application of this evolutionary search. Performance of the GA and the PSO were compared with the PSO producing the optimal results of being able to focus a 211 pulse ISAR image with second-order motion error in 9 s or 24% of the cost function calculations required for an exhaustive search. The PSO algorithm was then applied to a 211 pulse ISAR image with fourth-order motion error. The PSO algorithm was able to focus this image in 20 s with 33% of the cost function calculations required by the exhaustive search. This study also introduces a new method of determining basis function suitability using the fast Fourier transform. - Author(s): Y. Li ; Y. Fu ; X. Li ; L. Le-wei
- Source: IET Signal Processing, Volume 4, Issue 4, p. 343 –351
- DOI: 10.1049/iet-spr.2009.0046
- Type: Article
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When multiple radar targets are close to each other, the returned signals from these targets are overlapped in time. Therefore by applying conventional motion compensation algorithms designed for single target, the multiple targets cannot be resolved, and individual one cannot be clearly imaged. The authors conclude that whether the radar transmits linear frequency modulated (LFM) or stepped-frequency waveform, the chirp rate in the Doppler frequency shift induced by the translation motion is only concerned with the acceleration of the target. For different targets, the chirp rates are different from each other. Based on the different chirp rates, the signals from each target can be separated. Then a new algorithm based on the adaptive joint time frequency (AJTF) technique is proposed to separate the signals from different target in each cross-range cell. The use of the particle swarm optimisation (PSO) for multiple targets separation is a unique application of this evolutionary search. By the CLEAN technique, the number of targets need not be appointed. The simulation results confirm the efficiency of the proposed algorithm for multiple moving targets imaging. - Author(s): S. Stanković ; I. Orović ; A. Krylov
- Source: IET Signal Processing, Volume 4, Issue 4, p. 352 –362
- DOI: 10.1049/iet-spr.2009.0060
- Type: Article
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p.
352
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The two-dimensional multiwindow S-method for radar imaging applications is proposed. It represents a combined technique that uses the standard S-method and the multiple windows approach based on the two-dimensional Hermite functions. The proposed method provides significant improvement of radar image concentration in comparison with the standard S-method. Also, it does not require an additional post-processing algorithm. The efficiency of the proposed method is demonstrated through various examples. - Author(s): I. Orović ; S. Stanković ; T. Thayaparan ; LJ. Stanković
- Source: IET Signal Processing, Volume 4, Issue 4, p. 363 –370
- DOI: 10.1049/iet-spr.2009.0059
- Type: Article
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A new distribution that provides high concentration in the time–frequency domain is proposed. It is based on the S-method and multiwindow approach, where different order Hermite functions are employed as multiple windows. The resulting distribution will be referred to as the multiwindow S-method. It preserves favourable properties of the standard S-method, whereas the distribution concentration is improved by using Hermite functions of just a few first orders. The proposed distribution is appropriate for radar signal analysis, as it will be proven by experimental examples. - Author(s): I. Djurović ; C. Ioana ; T. Thayaparan ; L. Stanković ; P. Wang ; V. Popović ; M. Simeunović
- Source: IET Signal Processing, Volume 4, Issue 4, p. 371 –381
- DOI: 10.1049/iet-spr.2009.0065
- Type: Article
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p.
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A cubic-phase function evaluation technique for multicomponent frequency-modulated signals with non-overlapped components in the time–frequency (TF) plane is proposed. The proposed technique is based on the short-time Fourier transform. Cross-terms are removed or reduced in the same manner as in the case of the TF representation called the S-method. The proposed technique is applied for visualisation of signals in time-chirp-rate plane and parameter estimation of analytical and radar signals. In addition, a procedure for focusing SAR images by using estimated parameters is proposed in order to verify obtained results. - Author(s): T. Thayaparan ; W. Brinkman ; G. Lampropoulos
- Source: IET Signal Processing, Volume 4, Issue 4, p. 382 –394
- DOI: 10.1049/iet-spr.2009.0102
- Type: Article
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This study presents two algorithms for enhancing the capabilities of the adaptive joint time–frequency (AJTF) approach and the three-dimensional (3D) motion detection method, respectively, on inverse synthetic aperture radar (ISAR) imaging. The first method is a modified AJTF motion compensation algorithm that is able to employ more than two prominent point scatterers for translation and rotation motion compensation, respectively. The second algorithm enhances a current 3D motion detection method. This algorithm improves the computation time for identifying 3D motion in ISAR images and selects the imaging time intervals with the least 3D motion. With these enhanced detection algorithms, the authors have the ability to distinguish the time intervals when the target undergoes smooth two-dimensional (2D) motion from those containing more chaotic 3D motion. As a result, the authors can reliably detect those time intervals where 2D target motions are predominant to form well-focused ISAR images that are used in automatic target recognition applications. - Author(s): J. Wang ; P.E.C. Stone ; Y.-J. Shin ; R.A. Dougal
- Source: IET Signal Processing, Volume 4, Issue 4, p. 395 –405
- DOI: 10.1049/iet-spr.2009.0137
- Type: Article
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p.
395
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The integrity of the electric power cables is vital to the safety of an entire electrical system. To ensure the health of the cables, a technique is needed for both detecting/locating defects, and predicting hard defects before they occur. The theory and limitations of the classical wiring diagnostic techniques time domain reflectometry (TDR) and frequency domain reflectometry (FDR) are discussed. This study then introduces joint time-frequency domain reflectometry (JTFDR) as a unique solution for the cable diagnostics and prognostics. By employing an interrogating incident signal and advanced post-processing of the reflected signals, JTFDR is shown to be capable of overcoming those limitations. JTFDR is experimentally proven to be successful for detecting and locating both hard and incipient defects. The prognostic capabilities of JTFDR are also demonstrated via accelerated ageing tests of an electric power cable. By utilising the incident/reflected signal information in the time and frequency domains simultaneously, JTFDR is proven to be a more effective diagnostic technique than the classical TDR and FDR. JTFDR can also be used to monitor incipient defects and better predict hard defects before they occur. - Author(s): P.R. Kersten ; R.W. Jansen ; T.L. Ainsworth ; J.V. Toporkov ; M.A. Sletten
- Source: IET Signal Processing, Volume 4, Issue 4, p. 406 –412
- DOI: 10.1049/iet-spr.2009.0074
- Type: Article
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Synthetic aperture radar (SAR) image formation implicitly assumes that the backscattered returns arise from stationary isotropic targets. When objects move during the SAR integration time, signal anomalies appear that distort, displace and smear moving targets in the image. Although these anomalies degrade the image, they provide the information that allows analysis of the underlying target motion. Joint time–frequency analysis (JTFA) exploits these signal anomalies to estimate the motion of typical point targets. However, the weak, transient returns from water surface scatterers complicate standard JTFA estimates of water surface speeds. A time–frequency representation is applied based on the Capon's spectral estimation technique that allows joint analysis of multiple azimuth lines, thereby increasing the signal-to-clutter ratio of weak scatterers. The authors compare the time–frequency estimate, employing single-phase-centre SAR data, to along-track interferometric SAR estimates of the same flow and show that both the methods produce comparable results. The authors derive the JTFA equations and estimate water surface speed for data collected at a specific imaging geometry. This study highlights the feasibility of using a single-phase-centre SAR system to determine the motion of slow moving distributed targets representative of water flow. - Author(s): S. Gabarda and G. Cristóbal
- Source: IET Signal Processing, Volume 4, Issue 4, p. 413 –420
- DOI: 10.1049/iet-spr.2009.0125
- Type: Article
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p.
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The detection of events in seismic time series has been a subject of great interest during the last 30 years. Most of the works in this area were based on detecting special patterns or clusters in seismic data. The authors present here a event detection method based on a time–frequency analysis through the Wigner distribution (WD). The proposed method consists on defining an appropriate entropic measure through a suitable time–frequency distribution, acting as probability distribution function. It is known from previous studies in the field that the information entailed by time–frequency representations (TFR) of time signals can be explored by means of different Rényi entropy measures. The non-positivity character of the WD implies that the classical Shannon entropy cannot be used, and therefore it has been replaced by a generalised measure such as the Rényi entropy. However, owing to the existence of multiple TFR normalisations, the so-called quantum normalisation has been empirically selected here for this particular application. This method is based on the identification of the events as those temporal clusters having the highest amount of information (entropy). The method is described and applied to different earthquake signals and volcanic tremors, using both real and synthetic data. The results are compared to other existing event detection methods. - Author(s): L. Cohen
- Source: IET Signal Processing, Volume 4, Issue 4, p. 421 –427
- DOI: 10.1049/iet-spr.2009.0132
- Type: Article
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p.
421
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In many physical situations that involve wave propagation there is dispersion and attenuation. Examples include sonar in shallow water, underground radar, seismic wave propagation, fibre optics, among many others. The author shows that phase-space methods are particularly suited to study propagation with dispersion since in such situations the velocity of propagation is frequency dependent. Depending on the situation the phase space may be time–frequency or position–wavenumber. The author derive explicit expressions for the Wigner distribution for both cases and how it evolves with time are derived. The application to the propagation of noise fields is also discussed. - Author(s): J.M. O' Toole ; M. Mesbah ; B. Boashash
- Source: IET Signal Processing, Volume 4, Issue 4, p. 428 –437
- DOI: 10.1049/iet-spr.2009.0104
- Type: Article
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p.
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The discrete time–frequency matched filter should replicate the continuous time–frequency matched filter, but the methods differ. To avoid aliasing, the discrete method transforms the real-valued signal to the complex-valued analytic signal. The theory for the time–frequency matched filter does not consider the discrete case using the analytic signal. The authors find that the performance of the matched filter degrades when using the analytic, rather than real-valued, signal. This performance degradation is dependent on the signal-to-noise ratio and the signal type. In addition, the authors present a simple algorithm to efficiently compute the time–frequency matched filter. The algorithm with the real-valued signal, comparative to using the analytic signal, requires one-quarter of the computational load. Hence the real-valued signal – and not the analytic signal – enables an accurate and efficient implementation of the time–frequency matched filter. - Author(s): D.J. Nelson
- Source: IET Signal Processing, Volume 4, Issue 4, p. 438 –446
- DOI: 10.1049/iet-spr.2009.0089
- Type: Article
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The author addresses the problem of estimating the time varying delay of a signal observed by two or more receivers, and develop accurate and computationally efficient methods for processing these signals. The effectiveness of these methods is demonstrated on signals whose parameters are consistent with airborne receivers and a stationary ground emitter. The author proposes a solution to the time varying delay problem that is based on the phase of the short time cross-correlation function. This model is based on the exact signal model in which received signal differs from the transmitted signal by a time varying delay function. The process presented is based on previous work and provides enough accuracy in the estimated delay function that accurate Doppler velocity may be estimated from the estimated delay function. Moreover, the process is computationally efficient since it is based on the conventional correlation function and does not require estimation of a cross-ambiguity function or scale cross-ambiguity function surfaces on a fine Doppler lattice. - Author(s): N.J. Stevenson ; M. Mesbah ; B. Boashash
- Source: IET Signal Processing, Volume 4, Issue 4, p. 447 –456
- DOI: 10.1049/iet-spr.2009.0084
- Type: Article
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This study proposes a novel, composite time–frequency distribution (TFD) constructed using a multiple-view approach. This composite TFD utilises the intrinsic mode functions (IMFs) of the empirical mode decomposition (EMD) to generate each view that are then combined using the arithmetic mean. This process has the potential to eliminate the inter-component interference generated by a quadratic TFD (QTFD), as the IMFs of the EMD are, in general, monocomponent signals. The formulation of the multiple-view TFD in the ambiguity domain results in faster computation, compared to a convolutive implementation in the time–frequency domain, and a more robust TFD in the presence of noise. The composite TFD, referred to as the EMD-TFD, was shown to generate a heuristically more accurate representation of the distribution of time–frequency energy in a signal. It was also shown to have performance comparable to the Wigner–Ville distribution when estimating the instantaneous frequency of multiple signal components in the presence of noise. - Author(s): G. Okopal and P. Loughlin
- Source: IET Signal Processing, Volume 4, Issue 4, p. 457 –464
- DOI: 10.1049/iet-spr.2009.0083
- Type: Article
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In this study, moments of a signal as features for classification in active sonar systems are considered. The authors analyse the impact of propagation effects on the moment features, including the effect of random variability in certain channel parameters, such as target distance. Since the propagation model includes frequency-dependent effects that induce non-stationarities in the propagating signal, the authors use a time–frequency based approximation technique to analyse the moments. The results show how particular random channel effects increase the variability in the moment features, and thus provide some insight on the possible degradation in classification performance of the moment features.
Editorial: Time-Frequency Approach to Radar Detection, Imaging, and Classification
Focusing inverse synthetic aperture radar images with higher-order motion error using the adaptive joint-time–frequency algorithm optimised with the genetic algorithm and the particle swarm optimisation algorithm – comparison and results
ISAR imaging of multiple targets using particle swarm optimisation – adaptive joint time frequency approach
Two-dimensional Hermite S-method for high-resolution inverse synthetic aperture radar imaging applications
Multiwindow S-method for instantaneous frequency estimation and its application in radar signal analysis
Cubic-phase function evaluation for multicomponent signals with application to SAR imaging
Inverse synthetic aperture radar image focusing using fast adaptive joint time–frequency and three-dimensional motion detection on experimental radar data
Application of joint time–frequency domain reflectometry for electric power cable diagnostics
Estimating surface water flow speeds using time–frequency methods
Detection of events in seismic time series by time–frequency methods
Time–frequency approach to radar, sonar and seismic wave propagation with dispersion and attenuation
Accurate and efficient implementation of the time–frequency matched filter
Formulation of Doppler and cross-ambiguity processes based on delay only
Multiple-view time–frequency distribution based on the empirical mode decomposition
Moment feature variability in uncertain propagation channels
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