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Signal Processing, IET

Issue 6 • Date August 2012

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Displaying Results 1 - 10 of 10
  • Iterative design of one-dimensional efficient seismic Lp infinite impulse response f-x digital filters

    Page(s): 541 - 545
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (326 KB)  

    This study proposes a new technique, the Lp iterative reweighted least-square (IRLS) algorithm, to design efficient and accurate non-causal complex-valued seismic infinite impulse response (IIR) frequency-space (f-x) digital filters. Unlike earlier works where the problem is prefiltered and linearised, through this technique, the weights are updated by the trust-region-reflection (TRR) optimisation method, and the non-linear-weighted least-square IIR filter design problem is solved directly. The results of this study show smooth convergence empirically of the proposed IRLS-TRR algorithm for designing efficient complex-valued IIR f-x filter coefficients. Using the same specification parameters, the design for an IIR f-x filter of the order of N=2, a requirement needed for seismic wavefield extrapolation filters, outperforms the same filter designed using the IRLS prefiltering linearised algorithm at the expense of a few additional iterations and a justified running design time. View full abstract»

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  • Validity of whitening-matched filter approximation to the pareto coherent detector

    Page(s): 546 - 550
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (234 KB)  

    It has been observed that the coherent multilook detector for targets embedded within Pareto intensity clutter can be approximated by the Gaussian optimal detector, or whitening-matched filter, in a number of real data sets. These correspond to clutter returns obtained from the Defence Science and Technology Organisation's Ingara radar, operating in X-band, at high grazing angles and in a circular spotlight mode. This study will establish conditions under which this can be explained mathematically. The key to this is to apply Stein's method from probability theory to establish rules of thumb to determine the validity of the approximation. View full abstract»

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  • Pulse-repetition-interval transform-based vibrating target detection and estimation in synthetic aperture radar

    Page(s): 551 - 558
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (684 KB)  

    A novel algorithm is proposed for detecting and estimating vibrating targets in synthetic aperture radar (SAR) data based on a pulse-repetition-interval (PRI) transform. Azimuthal signals of vibrating targets can be modelled as sinusoidal frequency-modulated (SFM) ones. The algorithm utilises the resemblance between the Doppler spectrum of vibrating-target SFM signals (or ghost image) and a pulse train, and applies to the spectrum the PRI transform originally used for estimating PRIs of pulse trains. The algorithm can detect SAR vibrating targets under moderate signal-to-noise/clutter ratios, and is also capable of accurately estimating the vibration frequencies even if there are multiple targets in a single range cell. The algorithm proposed has been successfully applied to both simulated and quasi-real data, and compared with that of the autocorrelation method, showing its superiority. View full abstract»

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  • Hybrid non-linear differentiator design for a permanent-electro magnetic suspension maglev system

    Page(s): 559 - 567
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (444 KB)  

    In this study, to solve the vehicle-guideway resonance problem when a maglev train is suspended on an elastic beam at a low speed, a gap differentiator with global fast convergence is designed. The differentiator in possession of a simple algorithm but without obvious oscillation phenomenon, has a good ability of filtering out the noise. From the analysis of amplitude frequency and phase frequency characteristics of the differentiator, the authors point out that the parameter choice should be based on the linear differential unit, and the auxiliary non-linear unit implements phase compensation, so the differentiator has a nearly 90° phase advance in the effective frequency band. The digital gap differentiator goes through the functional simulation with algorithm level in the open-loop and hardware test in the loop independently. Finally, the designed gap differentiator is used in a closed-loop suspension control system of permanent-electro magnetic suspension -type maglev train, the theoretical analysis and simulation experiments show that the differentiator can restrain the resonance phenomenon on the elastic beam effectively. View full abstract»

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  • Ambiguity function based on the linear canonical transform

    Page(s): 568 - 576
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (405 KB)  

    The ambiguity function (AF) as an important tool for time-frequency analysis, has widely been used in radar signal processing, sonar technology and so on. It does well in analysing chirp signals. However, it fails in estimating the cubic phase (CP) signal, which is required in many applications. As the generalisation of the Fourier transform (FT), the linear canonical transform (LCT) has received much attention, and has found many applications in filter design, pattern recognition, optics and so on. In this study, the authors define a new kind of AF - the AF based on the LCT (LCTAF), which is proposed to estimate the CP signal. Some important properties of LCTAF are discussed, such as symmetry and conjugation property, shifting property and Moyal formula. The relationships between the LCTAF and other time-frequency analysis distributions are derived, including the classical AF, the Wigner distribution function (WDF) based on LCT, the short-FT and the wavelet transform. The linear canonical AF (LCAF) is another kind of AF. The authors also discuss its relation with AF and WDF and give some new properties of the LCAF. At last, the LCTAF is applied for estimating the CP signal. The simulation indicates that the LCTAF is useful and effective. View full abstract»

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  • Performance analysis of cooperative diversity networks with imperfect channel estimation over Rician fading channels

    Page(s): 577 - 583
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (341 KB)  

    In this study, the authors examine the effect of a channel-estimation error on the error and outage probabilities of a multi-relay system with amplify-and-forward relaying over a frequency-flat Rician fading channel. The authors consider orthogonal relaying and study both conventional cooperative systems (i.e. all relays participate in the relaying phase) and opportunistic cooperative systems (i.e. only the best relay participates in the relaying phase). Based on the derivation of an effective signal-to-noise ratio (SNR) at the destination node taking into account channel-estimation error, the authors obtain closed-form expressions for error and outage probabilities in a high SNR regime. Such closed-form solutions are highly desirable because they allow for rapid and efficient evaluation of system performance. Computer simulations are used to validate the authors' analytical results. View full abstract»

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  • Optimality of greedy policy for a class of standard reward function of restless multi-armed bandit problem

    Page(s): 584 - 593
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (209 KB)  

    In this study, the authors consider the restless multi-armed bandit problem, which is one of the most well-studied generalisations of the celebrated stochastic multi-armed bandit problem in decision theory. However, it is known to be PSPACE-Hard to approximate to any non-trivial factor. Thus, the optimality is very difficult to obtain because of its high complexity. A natural method is to obtain the greedy policy considering its stability and simplicity. However, the greedy policy will result in the optimality loss for its intrinsic myopic behaviour generally. In this study, by analysing one class of so-called standard reward function, the authors establish the closed-form condition about the discounted factor β such that the optimality of the greedy policy is guaranteed under the discounted expected reward criterion, especially, the condition β=1 indicating the optimality of the greedy policy under the average accumulative reward criterion. Thus, this kind of standard reward function can easily be used to judge the optimality of the greedy policy without any complicated calculation. Some examples in cognitive radio networks are presented to verify the effectiveness of the mathematical result in judging the optimality of the greedy policy. View full abstract»

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  • Aliased polyphase sampling associated with the linear canonical transform

    Page(s): 594 - 599
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (252 KB)  

    The aliased polyphase sampling in the linear canonical transform (LCT) domain has been proposed in this study. The result shows that the sampling theorem can be achieved by using parallel samplers to obtain a higher sampling rate in the LCT domain. Further analysis shows that the LCT spectrum of x(nM+m) replicates |X(a,b,c,d)(u)| with a period bMFs along with linear phase modulation in the LCT domain, where b is one parameter of the LCT, M is the number of samplers and Fs is the sampling frequency at which these samplers are operated in the Fourier transform domain. Finally, the simulations are also proposed to verify the correctness of the derived results. View full abstract»

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  • Evaluation of correlation functions and design of minimum mean squared error equaliser for a high-density magnetic recording channel with partial erasure effect

    Page(s): 600 - 607
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (398 KB)  

    In a high-density magnetic recording channel, non-linear effects such as transition shift and partial erasure arise, and these effects limit detector performance. The transition shift can be precompensated by using an appropriate write current; however, partial erasure still degrades detector performance. In this study, the authors obtained the correlation functions of the stored data in the presence of partial erasure under the assumption that the transition shift has been precompensated. The correlation functions are applied in designing a minimum mean squared error equaliser for both the linear partial response and the non-linear partial response, and the corresponding partial response maximum likelihood (PRML) sequence detector, realised by the Viterbi decoder, is also examined. Computer simulations indicate that the mean squared error of the proposed equaliser can be improved and the bit error rate of the PRML sequence detector is also reduced as compared with the conventional one in the presence of partial erasure. View full abstract»

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  • Dynamic bayesian modelling of non-stationary stochastic systems using constrained least square estimation and gradient descent optimisation

    Page(s): 608 - 615
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (486 KB)  

    A dynamic Bayesian network (DBN) is a statistical tool particularly for representing stochastic casual systems using probability and graph theories. The most important procedure in constructing a DBN is selecting the best parameter vector given as conditional probability distribution through a proper learning algorithm. This study presents a novel parameter learning methodology for Markov chain (MC) and hidden Markov model (HMM) DBN using the constrained least square method and the gradient descent optimisation, respectively. The former is employed for satisfying the probability axiom in an MC model and the latter is applied to derive adjustment rules for HMM parameters. The authors primitively assume that an observation probability vector is necessarily predefined prior to applying of the proposed learning algorithm for both models. Simulation experiment is achieved to test their learning algorithm for modelling non-stationary stochastic systems. The authors additionally provide qualitative comparative study with recently addressed learning methodologies of DBN models. View full abstract»

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