IET Signal Processing (IET SIGNAL PROCESS )

Publisher: Institution of Engineering and Technology

Description

IET Signal Processing publishes novel contributions in signal processing including: advances in single and multi-dimensional filter design and implementation; linear and nonlinear, fixed and adaptive digital filters and multirate filter banks; statistical signal processing techniques and analysis; classical, parametric and higher order spectral analysis; signal transformation and compression techniques, including time-frequency analysis; system modelling and adaptive identification techniques; machine learning based approaches to signal processing; Bayesian methods for signal processing, including Monte-Carlo Markov-chain and particle filtering techniques; theory and application of blind and semi-blind signal separation techniques; signal processing techniques for analysis, enhancement, coding, synthesis and recognition of speech signals; direction-finding and beamforming techniques for audio and electromagnetic signals; analysis techniques for biomedical signals; baseband signal processing techniques for transmission and reception of communication signals; signal processing techniques for data hiding and audio watermarking.

  • Impact factor
    0.71
    Show impact factor history
     
    Impact factor
  • 5-year impact
    0.80
  • Cited half-life
    2.90
  • Immediacy index
    0.04
  • Eigenfactor
    0.00
  • Article influence
    0.27
  • Website
    IET Signal Processing website
  • Other titles
    Signal processing
  • ISSN
    1751-9675
  • OCLC
    123759426
  • Material type
    Internet resource
  • Document type
    Journal / Magazine / Newspaper, Internet Resource

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: The diagonal loading (DL) technique is the most widely used method to improve the robustness of the Capon beamformer in the presence of imprecise knowledge of the covariance matrix and the desired signal’s steering vector. The selection of the DL level is challenging in practice and might depend on some user-defined parameters which are possibly hard to be determined. A fully automatic and training-free method for the DL level selection is herein presented to extract the desired signal with constant modulus, which is a common feature for communication signals. Simulated results of the beamforming performance have demonstrated the efficacy of the proposed method.
    IET Signal Processing 12/2014;
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    ABSTRACT: This paper describes a new multilevel decomposition method for the separation of convolutive image mixtures. The proposed method uses an Adaptive Quincunx Lifting Scheme (AQLS) based on wavelet decompo- sition to preprocess the input data, followed by a Non-Negative Matrix Factorization whose role is to unmix the decomposed images. The un- mixed images are, thereafter, reconstructed using the inverse of AQLS transform. Experiments carried out on images from various origins showed that the proposed method yields better results than many widely used blind source separation algorithms.
    IET Signal Processing 07/2014;
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    ABSTRACT: Unlike synchronous processing, asynchronous processing is more efficient in biomedical and sensing networks applications as it is free from aliasing constraints and quantization error in the amplitude, it allows continuous–time processing and more importantly data is only acquired in significant parts of the signal. We consider signal decomposers based on the asynchronous sigma delta modulator (ASDM), a non-linear feedback system that maps the signal amplitude into the zero-crossings of a binary output signal. The input, the zero-crossings and the ASDM parameters are related by an integral equation making the signal reconstruction difficult to implement. Modifying the model for the ASDM, we obtain a recursive equation that permits to obtain the non-uniform samples from the zero-time crossing values. Latticing the joint time-frequency space into defined frequency bands, and time windows depending on the scale parameter different decompositions are possible. We present two cascade low- and high-frequency decomposers, and a bank-of-filters parallel decomposer. This last decomposer using the modified ASDM behaves like a asynchronous analog to digital converter, and using an interpolator based on Prolate Spheroidal Wave functions allows reconstruction of the original signal. The asynchronous approaches proposed here are well suited for processing signals sparse in time, and for low-power applications.
    IET Signal Processing 05/2014;
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    ABSTRACT: We propose the discrete linear chirp transform (DLCT) for decomposing a non-stationary signal into intrinsic mode chirp functions. The decomposition of a signal into a finite number of intrinsic mode functions (IMFs) was introduced by the empirical mode decomposition (EMD). It exploits the local time-scale signal characteristics of the signal and provides spectral estimates obtained via the Hilbert transform. Although efficient, the EMD does not provide an analytic representation of the IMFs and is susceptible to noise and to closeness or overlap of the frequency of the IMFs. Using linear chirps as IMFs, the DLCT, a joint frequency instantaneous frequency procedure, provides a parsimonious local orthogonal representation of non-stationary signals. Moreover, the DLCT allows a parametric estimation of the instantaneous frequency of the signal that is robust to noise and to closeness or overlap in the instantaneous frequency of the modes. More importantly, the DLCT can be used to represent and process signals that are sparse in a joint time–frequency sense. The performance of the DLCT and the EMD are illustrated and compared when used to estimate the instantaneous frequency of individual signal components, to obtain signal decompositions at different frequency bands and to process frequency modulated signals with time-varying amplitude.
    IET Signal Processing 05/2014;
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    ABSTRACT: The accuracy of sources locations and velocities estimate is very sensitive to the accurate knowledge of sensor locations and velocities. In the presence of sensor position and velocity errors, this study considers the problem of simultaneously locating multiple disjoint sources and refining erroneous sensor positions and velocities using time differences of arrival and frequency differences of arrival. The previous work by Sun and Ho to solve this problem provided an efficient estimator for multiple disjoint sources, but it cannot provide optimum accuracy for the sensor positions and sensor velocities. In many practical applications, it is necessary and helpful to refine sensor locations and velocities while localising multiple sources. The proposed method improves the previous method so that both the source and the sensor position and velocity estimates can achieve the Cramér–Rao lower bound accuracy very well over small noise region. The theoretical derivation is corroborated by simulations.
    IET Signal Processing 04/2014; 8(2):13.
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    ABSTRACT: A general method for efficient computation of four types of discrete Hartley transform using cyclic convolutions is considered. Forming hashing arrays on the basis of simplified arguments of basis transform for synthesis of efficient algorithm is analysed. The hashing arrays in the algorithm define partitioning of the harmonic basis into Hankel submatrices. The examples of four types of discrete Hartley transforms using the proposed method are presented.
    IET Signal Processing 01/2014; 8(4):301-308.
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    ABSTRACT: In this study, a competitive linear parallel interference cancellation (LPIC) detector that is memory efficient, enjoys fast convergence and low complexity and can be used to approximate the decorrelator/minimum-mean-square error detector in largescale communication systems is proposed. Similar to the non-monotone gradient-based LPIC detectors developed recently by Bentrcia and Alshebeili, the proposed detector maintains its efficiency and does not break down quickly if matrix??vector products are not performed accurately. However, unlike the previous detectors, the proposed detector relies on a monotone line-search technique which renders it more attractive because early stopping methods such as the L-curve method can be used to stop the LPIC iterations prior to convergence in order to avoid the noise enhancement effect. Simulation results agree well with the authors theoretical findings.
    IET Signal Processing 01/2014; 8(5):521-529.
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    ABSTRACT: This study describes a scheme which enables one to improve the quality of one's own wireless communications, over a given frequency (or frequencies), when in the presence of inter-modulation distortion (IMD). The IMD is generated by one's own power amplifier (PA), when operating over an adjacent band of frequencies, and arises as a result of the non-linear nature of the PA when engaged in the transmission of modulated multi-carrier signals. The distortion appears in the form of inter-modulation products (IMPs), these occurring at multiple frequencies which may potentially coincide with one's communication frequency. The scheme enables one to predict the frequency locations and strengths of the IMPs and, when coincident with the communication frequency, to clear the IMPs from that frequency regardless of the levels of distortion present. The speed at which the IMPs are identified and cleared from the communication frequency - attributable to the efficient exploitation of polynomial arithmetic/algebraic techniques and a fast Fourier transform routine - offers the promise of maintaining reliable communications without having to interrupt the operation of one's own electronic equipment. The low complexity also offers the possibility of an attractive hardware solution with a low size, weight and power requirement.
    IET Signal Processing 01/2014; 8(5):495-506.
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    ABSTRACT: The article studies the steady-state performance of a diffusion least-mean squares (LMS) adaptive network with imperfect communications where the topology is random (links may fail at random times) and the communication in the channels is corrupted by additive noise. Using the established weighted spatial-temporal energy conservation argument, the authors derive a variance relation which contains moments that represent the effects of noisy links and random topology. The authors evaluate these moments and derive closed-form expressions for the mean-square deviation, excess mean-square error and mean-square error to explain the steady-state performance at each individual node. The mean stability analysis is also provided. The derived theoretical expressions have good match with simulation results. Nevertheless, the important result is that the noisy links are the main factor in performance degradation of a diffusion LMS algorithm running in a network with imperfect communications.
    IET Signal Processing 01/2014; 8(1):59-66.
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    ABSTRACT: Two high-resolution direction of arrival (DOA) estimation approaches of non-stationary narrowband signals based on matching pursuit (MP) are developed. The first sensor output is considered as the reference and decomposed by MP. As the MP is a linear decomposition, the obtained MP coefficients contain the steering vector information. So, the MP coefficients corresponding to the leading decomposition atoms are used to develop the MP-MUSIC algorithm for the DOA estimation. In addition, the chosen MP atoms are used to implement the modified spatial time??frequency distribution (STFD) based on Wigner Ville (WV) distribution as well, and this method named MP-WV. It has been demonstrated that these two methods can be applied for underdetermined problems and are robust against Gaussian and impulsive noises. The authors show that using either coefficients or chosen atoms to estimate the DOA in array processing by considering the source discriminative capability outperforms the conventional MUSIC and STFD. Some simulation results showing the performance of the two proposed approaches based on MP, conventional MUSIC and STFD are presented.
    IET Signal Processing 01/2014; 8(5):540-551.
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    ABSTRACT: The presence of the desired signal (DS) in the training snapshots makes the adaptive beamformer sensitive to any steering vector mismatch and dramatically reduces the convergence rate. Even the performance of the most of the existing robust adaptive beamformers is degraded when the signal-to-noise ratio (SNR) is increased. In this study, a high converging rate robust adaptive beamformer is proposed. This method is a promoted eigenspace-based beamformer. In this paper, a new signal-plus-interferences (SPI) covariance matrix estimator is proposed. The subspace of the ideal SPI covariance matrices is exploited and the estimated covariance matrix is projected into this subspace. This projection effectively reduces the covariance matrix estimation error and the proposed estimator yields a more accurate estimation of the SPI covariance matrix. In addition, a computationally efficient steering vector estimator has been proposed. To prevent the absence of the DS steering vector in the estimated SPI subspace, the estimated SPI covariance matrix is compensated. Hence, the proposed method can attain the optimal beamformer in the both high and low SNR cases. The numerical examples indicate that this method has excellent signal-to-interference plus noise ratio performance and offers a higher converging rate compared with the existing robust adaptive beamforming algorithms.
    IET Signal Processing 01/2014; 8(5):507-520.
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    ABSTRACT: In recent years, wavelet packet modulation-orthogonal frequency division multiplexing (WPM-OFDM) signals have been introduced for radar applications. These signals have some significant properties such as inherent high range resolution, high resistance of radar system against jamming reception and improved target detection performance in contrast with common traditional signals. However, there is no systematic method for designing WPM-OFDM signals to be used in radar applications. In the present study, the authors have started solving the problem of designing a WPM-OFDM radar signal under a criterion of minimising the least-squares error between designed and desired ambiguity functions. A thumbtack shape is assumed to be the ideal shape of the ambiguity function. In the following, an iterative algorithm is introduced to allocate a proper phase to the desired ambiguity function for obtaining better results. In this study, it is shown that this algorithm can reduce side-lobes, throughout the entire plane of the ambiguity function; therefore using the mentioned algorithm leads to having a desired signal for a radar application. Consequently, by extending the presented method, a pair of WPM-OFDM signals is simultaneously designed which obtain their cross ambiguity function to approximate a desired one under the criterion of least-squares.
    IET Signal Processing 01/2014; 8(5):475-482.
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    ABSTRACT: The authors propose novel spectrum sensing and utilisation schemes for cognitive radio networks using the chirp-z transform. To improve the spectral efficiency, a dispersed chirp-z transform is introduced with the energy detection method. The dispersed chirp-z transform enables one to analyse the dispersed frequency spectrum of any frequency range of interest. The analysis is first focused on the sensing part, including the derivations of closed-form expressions for the optimal detection thresholds minimising the total error rate over additive white Gaussian noise (AWGN), Rayleigh, Rician and lognormally distributed fading channels. Then, the performance analysis of the proposed system is investigated for efficient spectrum utilisation over AWGN and fading channels by presenting the receiver operating characteristics. Conventional and segmented chirp-z transform-based spectrum utilisation techniques are also introduced for performance comparison purposes. Finally, the theoretical framework and the optimal threshold derivations through simulations are verified. The analyses reveal that the proposed schemes have a considerable potential to improve the non-cooperative spectrum sensing and utilisation performance.
    IET Signal Processing 01/2014; 8(4):320-329.
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    ABSTRACT: Detection of manoeuvring weak targets in radars often encounters circumstance where target movement is modelled by non-linear dynamic systems and received returns are corrupted by background noise of unknown statistics. It is known that the cost-reference particle filter (CRPF) is an efficient algorithm for state estimation of non-linear dynamic systems of unknown statistics. By combining an approximate logarithm likelihood ratio under the piecewise parametric model of signals with the CRPF algorithm, this study proposes a new track-before-detect detector, named CRPF-based detector, for manoeuvring weak target detection from received returns corrupted by background noise of unknown statistics. Experiments using simulated noise and real background noise of over-the-horizon radar are made to verify the CRPF-based detector. The results show that the CRPF-based detector has comparable performance with the two PF-based detectors for background noise of known statistics. For background noise of unknown statistics, the CRPF-based detector attains better detection performance than the two PF-based detectors where an assumptive probabilistic model is imposed on the background noise.
    IET Signal Processing 01/2014; 8(1):85-94.
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    ABSTRACT: Joint estimation of the random impairments, phase noise (PHN) and channel, in orthogonal frequency division multiplexing (OFDM) system is investigated in this study. Bayesian Cram??r-Rao lower bounds (BCRLBs) for the joint estimation of PHN and channel are derived, and are compared with the corresponding standard CRLB, which shows the significance of joint estimator in a Bayesian framework. The authors propose maximum a posteriori algorithms for the estimation of PHN and channel, utilising their statistical knowledge which is known a priori. The performance of the estimation methods is studied through simulations and numerical results show that the performance of the proposed algorithms is better than existing algorithms and is closer to BCRLB.
    IET Signal Processing 01/2014; 8(1):10-20.
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    ABSTRACT: This study presents a discussion on the task of score alignment, which properly aligns an audio recording with its corresponding score. Conventional methods have difficulty performing this task because of asynchrony in the recording of simultaneous notes in the score. A note-based score alignment based on the pitch-by-time feature is proposed, called the piano-roll feature, and it presents an approach for converting the audio spectrogram to a piano-roll-like feature. Score-driven non-negative matrix factorisation is then adopted in the transformation. Furthermore, this study also proposes pitch-wise alignment considering each pitch sequence (i.e. the row of piano roll) separately. Results based on the MIDI-Aligned Piano Sounds database show that approximately 88% of notes match their onsets, deviating from the ground truth by less than 50 ms. Other results based on SCREAM Music Annotation Project database that is a manual annotation project of commercial CD recordings are presented as well.
    IET Signal Processing 01/2014; 8(1):1-9.
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    ABSTRACT: The bias compensation technique combined with the least-squares estimation algorithm with forgetting factors is applied to the parameter estimation of output error models with moving average noise. It is shown that the bias term induced by the noise is determined by the weighted average variance of the white noise and the parameters of the unknown noise model. Therefore, in order to give a recursive estimation of the bias term, an interactive estimation of the weighted average variance and noise parameters is constructed by using the principle of hierarchical identification. In addition, a recursive form is also established to estimate the so-called weighted average variance of the white noise. The estimation algorithm is finally established by combining the interactive estimation and the recursive estimation of weighted average variance. A simulation example is employed to show the effectiveness of the proposed bias compensation based least-squares estimation algorithm with two forgetting factors.
    IET Signal Processing 01/2014; 8(5):483-494.
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    ABSTRACT: This study presents a novel spatial audio coding (SAC) technique, called analysis by synthesis SAC (AbS-SAC), with a capability of minimising signal distortion introduced during the encoding processes. The reverse one-to-two (R-OTT), a module applied in the MPEG Surround to down-mix two channels as a single channel, is first configured as a closed-loop system. This closed-loop module offers a capability to reduce the quantisation errors of the spatial parameters, leading to an improved quality of the synthesised audio signals. Moreover, a sub-optimal AbS optimisation, based on the closed-loop R-OTT module, is proposed. This algorithm addresses a problem of practicality in implementing an optimal AbS optimisation while it is still capable of improving further the quality of the reconstructed audio signals. In terms of algorithm complexity, the proposed sub-optimal algorithm provides scalability. The results of objective and subjective tests are presented. It is shown that significant improvement of the objective performance, when compared to the conventional open-loop approach, is achieved. On the other hand, subjective test show that the proposed technique achieves higher subjective difference grade scores than the tested advanced audio coding multichannel.
    IET Signal Processing 01/2014; 8(1):30-38.
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    ABSTRACT: In this study, a novel method for target identification in a foliage environment is presented. This method is based on the ultra wideband (UWB) wireless sensor networks (WSNs) model, and the foliage environment is specially considered. The data used to identify the targets are derived from the received signal waveform, so most existing transceivers can be exploited as detecting sensors, which leads to a potential low-cost way to identify targets during the normal communications within the WSNs under foliage environment. The selected bispectra algorithm is applied to extract the feature vector, and chaos particle swarm optimisation-based support vector machine is used as the target classifier. Experiments with real-world data samples indicate that this method has an excellent classification performance in a foliage environment. Moreover, this method shows potential for online training.
    IET Signal Processing 01/2014; 8(1):76-84.

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