By Topic

Signal Processing, IET

Issue 6 • Date September 2011

Filter Results

Displaying Results 1 - 12 of 12
  • Recursive spectral analysis of natural time series based on eigenvector matrix perturbation for online applications

    Page(s): 515 - 526
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (2458 KB)  

    Singular spectrum analysis (SSA) is a well-studied approach in signal processing. SSA has originally been designed to extract information from short noisy chaotic time series and to enhance the signal-to-noise ratio. SSA is good for offline applications; however, many applications, such as modelling, analysis, and prediction of time-varying and non-stationary time series, demand for online analysis. This study introduces a recursive algorithm called recursive SSA as a modification to regular SSA for dynamic and online applications. The proposed method is based on eigenvector matrix perturbation approach. After recursively calculating the covariance matrix of the trajectory matrix, R-SSA updates the eigenvalues and eigenvectors for new samples by considering the effect of the new sample as perturbation in the covariance matrix and its singular value decomposition. The eigenvalues and eigenvectors adapt simultaneously to track their true values as would be calculated from the current covariance matrix. Analysis of two well-known chaotic time series: Mackey-Glass and Lorenz chaotic time series and two natural time series: Darwin sea-level pressure and Sunspot number as non-stationary processes are considered in this study to examine the performance of the proposed recursive method. The obtained results depict the power of the proposed method in online spectral analysis of non-linear time-varying systems. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Segmentation and identification of some pathological phonocardiogram signals using time-frequency analysis

    Page(s): 527 - 537
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1156 KB)  

    Heart sounds that are multicomponent non-stationary signals characterise the normal phonocardiogram (PCG) signals and the pathological PCG signals. The time-frequency analysis is a powerful tool in the analysis of non-stationary signals especially for PCG signals. It permits detecting and characterising abnormal murmurs in the diagnosis of heart disease. In this study, the authors introduce a novel method based on time-frequency analysis in conjunction with a threshold evaluated on Renyi entropy for the segmentation and the analysis of PCG signals. The method was applied to different sets of PCG signals: early aortic stenosis, late systolic aortic stenosis, pulmonary stenosis and mitral regurgitation. The analysis has been conducted on real biomedical data. Tests performed proved the ability of the method for segmentation between the main components and the pathological murmurs of the PCG signal. Also, the method permits elucidating and extracting useful features for diagnosis and pathological recognition. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Process noise identification based particle filter: an efficient method to track highly manoeuvring targets

    Page(s): 538 - 546
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (386 KB)  

    In this study, a novel method, process noise identification-based particle filter is proposed for tracking highly manoeuvring target. In the proposed method, the equivalent-noise approach is adopted, which converts the problem of manoeuvring target tracking to that of state estimation in the presence of non-stationary process noise with unknown statistics. A novel method for identifying the non-stationary process noise is proposed in the particle filter framework. Compared with the multiple model approaches for manoeuvring target tracking, the proposed method needs to know neither the possible multiple models nor the transition probability matrices. One simple dynamic model is adopted during the whole tracking process. The proposed method is especially suitable for tracking highly manoeuvring target because of its capability of dealing with sample impoverishment, which is a common problem in particle filter and becomes serious when tracking large uncertain dynamics. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Digital fractional delay implementation based on fractional order system

    Page(s): 547 - 556
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (680 KB)  

    This study presents a design method of the digital fractional delay operator z-m for 0<;m<;1, in a given frequency band of interest, using digital infinite impulse response (IIR) filters. The design technique is based on the approximation of the fractional order systems. First, analogue rational function approximation, for a given frequency band, of the fractional power pole (FPP) is given. Then the forward difference generating function is used to digitise the FPP to obtain a closed-form IIR digital filter, which approximates the digital fractional delay operator z-m for 0<;m<;1. Finally, an example is presented to illustrate the effectiveness of the proposed design method. The proposed technique has also been used to design a comb filter. The results obtained have been discussed and compared with some of the most recent work in the literature. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Priority wavelet packet decomposition and representation

    Page(s): 557 - 567
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (531 KB)  

    Wavelet packets (WPs) offer a general framework for representing an arbitrary signal efficiently. But the associated computational cost of finding an optimal WP basis is quite high. To address this problem, the authors introduce, first, a signal-conditioning-based spectral density-driven wavelet transform (SDDWT). The structure of implementing SDDWT is similar to that of discrete wavelet transform (DWT) but it provides better approximation performance than that offered by DWT for a dominantly band-pass signal, including low pass. Then, for arbitrary signal, a generalisation of SDDWT, which the authors call as spectral density-driven wavelet packet (SDDWP) transform is introduced. The resultant bands have certain priority according to their ability to reduce the reconstruction error. The SDDWP can be considered a near-optimal WP basis but is also amenable to fast optimal WP basis search, at much reduced computational cost. Also, it not only provides improved approximation performance but in this case the complexity of implementing the transformation can be controlled. A side effect of the proposed transformation is the computation of signal-conditioning information, for which an efficient algorithm is provided. Simulations results have shown improved approximation performance. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Quadratic time-frequency analysis and sequential recognition for specific emitter identification

    Page(s): 568 - 574
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (431 KB)  

    Specific emitter identification (SEI) is a state-of-the-art in electronic warfare. The conventional methods for SEI hardly satisfy the modern electronic reconnaissance. In this study, first the authors study the quadratic time-frequency distributions and its slice features and noise analysis. Based on the time-frequency features, two sequential recognition methods based on probabilistic support vector machine (SVM) and iterative least-square estimation are studied, respectively. The proposed methods are able to reject the interference pulses and update the classifier or feature parameters, which accomplish the online recognition and online learning of specific radar emitter. Experiments on actual intercepted radar signals with the same type verify the correctness and validity of the proposed methods. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • ECG beat classification using features extracted from teager energy functions in time and frequency domains

    Page(s): 575 - 581
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (320 KB)  

    It is hypothesised that a key characteristic of ECG signal is its non-linear dynamic behaviour and that the non-linear component changes more significantly between normal and arrhythmia conditions than the linear component. This study makes an attempt to analyse ECG beats from an energy point of view by accounting for the features derived from non-linear component in time and frequency domains using Teager energy operator (TEO). The key feature of TEO is that it models the energy of the source that generated the signal rather than the energy of the signal itself. Hence any deviations in the regular rhythmic activity of the heart get reflected in the Teager energy function. To show the validity of appropriate choice of features, t-tests and scatter plot are used. The Mests show significant statistical differences and scatter plot of mean of Teager energy in time domain against mean of Teager energy in frequency domain for the ECG beats evaluated on selected Manipal Institute of Technology-Beth Israel Hospital (MIT-BIH) database, which reveals an excellent separation of the features into five different classes: normal, left bundle branch block, right bundle branch block, premature ventricular contraction and paced beats. The neural network results achieved through only two non-linear features exhibit an average accuracy that exceeds 95%, average sensitivity of about 80% and average specificity of almost 100%. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Beamforming and temporal power optimisation for an overlay cognitive radio relay network

    Page(s): 582 - 588
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (408 KB)  

    The authors consider co-existence of a secondary network with a primary network under an overlay framework and propose beamformer design and power allocation using an iterative optimisation technique. The secondary network serves multiple users in the same frequency band as of the primary network; however, in order to compensate the interference leakage to the primary user terminals, the secondary network acts as a relay to forward the primary user signals. The interference and noise level at the primary terminals during various time slots are different; therefore the primary network needs to allocate resources asymmetrically during various time slots for the optimal performance. Hence, the authors have proposed a joint spatial and temporal resource allocation technique to enhance the overall system power saving while satisfying the data rate or the signal-to-interference plus noise ratio requirement of the primary and secondary users. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Stochastic gradient identification of Wiener system with maximum mutual information criterion

    Page(s): 589 - 597
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (478 KB)  

    This study presents an information-theoretic approach for adaptive identification of an unknown Wiener system. A two-criterion identification scheme is proposed, in which the adaptive system comprises a linear finite-impulse response filter trained by maximum mutual information (MaxMI) criterion and a polynomial non-linearity learned by traditional mean square error criterion. The authors show that under certain conditions, the optimum solution matches the true system exactly. Further, the authors develop a stochastic gradient-based algorithm, that is, stochastic mutual information gradient-normalised least mean square algorithm, to implement the proposed identification scheme. Monte-Carlo simulation results demonstrate the noticeable performance improvement of this new algorithm in comparison with some other algorithms. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Signal position-based adaptive QR decompositionbased M detection algorithm for multiple-input multiple-output systems

    Page(s): 598 - 602
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (510 KB)  

    The QR decomposition-based M (QRD-M) algorithm is a suboptimal detection algorithm that achieves near-maximum-likelihood detection (MLD) performance with low complexity. Some conventional QRD-M algorithms that directly determine the number of surviving paths have simple structures and are consequently cost effective and easy to implement. However, it is possible that surviving paths are chosen that have unnecessarily low reliability. The authors propose an algorithm, termed signal position-based QRD-M, to solve this problem using position estimation of the received signal. The position estimation process is accomplished using the most reliable candidate symbol to minimise additional processing. Computer simulations demonstrated that the proposed algorithm can achieve near-MLD performance with lower complexity than the fixed QRD-M algorithm. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Probability of even parity of soft bits

    Page(s): 603 - 611
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (457 KB)  

    Gallager's lemma, presented originally in the context of decoding low-density parity check (LDPC) codes, computes the probability of even parity for independent, non-identically distributed bits. As the number of bits involved in the parity increases, there is a tendency of the probability of even parity towards 1/2. This tendency is discussed and analysed, resulting in a sort of counter central limit theorem result. In many instances, there are practical limits on the number of bits that can be included in a reliable soft parity computation. Implications affect LDPC decoding, soft descrambling and soft encoding of data. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Joint source-and-relay power allocation in multipleinput multiple-output amplify-and-forward relay systems: a non-convex problem and its solution

    Page(s): 612 - 622
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (498 KB)  

    In this study, the joint source-and-relay power allocation (JPA) problem in multiple-input multiple-output (MIMO) relay systems is investigated. It is revealed for the first time that the JPA problem based on the mean squared error (MSE) minimisation or the achievable rate maximisation criterion possesses a partial convexity, that is, the cost function is convex with respect to (w.r.t.) part of the optimisation parameters only. It is then proved that a better partial convexity, namely, a partial convexity w.r.t. a larger subset of the optimisation parameters, can be achieved by relaxing the cost function with an upper or lower bound of the MSE or achievable rate. By exploiting the partial convexity properties disclosed, two iterative algorithms are proposed to solve the non-convex JPA problem. It is shown that the convergence of the proposed iterative algorithms is guaranteed because of the fact that each iteration follows a convex optimisation w.r.t. the partial parameters. Finally, it is shown by Monte Carlo simulations that the proposed algorithms outperform several existing methods including our own prior work. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.

Aims & Scope

IET Signal Processing publishes novel contributions in signal processing.

Full Aims & Scope

Meet Our Editors

IET Research Journals
iet_spr@theiet.org