IET Signal Processing
Volume 8, Issue 5, July 2014
Volumes & issues:
Volume 8, Issue 5
July 2014
Adaptive genetic algorithm-based approach to improve the synthesis of two-dimensional finite impulse response filters
- Author(s): Kamal Boudjelaba ; Frédéric Ros ; Djamel Chikouche
- Source: IET Signal Processing, Volume 8, Issue 5, p. 429 –446
- DOI: 10.1049/iet-spr.2013.0005
- Type: Article
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The design of finite impulse response (FIR) filters can be formulated as a non-linear optimization problem reputed to be difficult for conventional approaches. The constraints are high and a large number of parameters have to be estimated, especially when dealing with two-dimensional FIR filters. In order to improve the performance of conventional approaches, the authors explore several stochastic methodologies capable of handling large spaces. The authors specifically propose a new genetic algorithm (GA) in which some innovative concepts are introduced to improve the convergence and make its use easier for practitioners. The algorithm is globally improved by adapting the mutation and crossover and selection operators with the genetic advances. A dynamic ranking selection scheme is introduced to limit the promotion of extraordinary chromosomes. A refreshing mechanism is investigated to manage the trade-off between diversity and elitism. The key point of the proposed approach stems from the capacity of the GA to adapt the genetic operators during the genetic life while remaining simple and easy to implement. Most of the parameters and operators are changed by the GA itself. From an initial calibration, the GA performs the design problem while calibrating and repeatedly re-calibrating itself for solving it. The authors demonstrate on various cases of filter design a significant improvement in performance.
Robust adaptive beamforming algorithms using the constrained constant modulus criterion
- Author(s): Lukas Landau ; Rodrigo C. de Lamare ; Martin Haardt
- Source: IET Signal Processing, Volume 8, Issue 5, p. 447 –457
- DOI: 10.1049/iet-spr.2013.0166
- Type: Article
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The authors present a robust adaptive beamforming algorithm based on the worst-case (WC) criterion and the constrained constant modulus (CCM) approach, which exploits the constant modulus property of the desired signal. Similar to the existing worst-case beamformer with the minimum variance design, the problem can be reformulated as a second-order cone programme and solved with interior point methods. An analysis of the optimisation problem is carried out and conditions are obtained for enforcing its convexity and for adjusting its parameters. Furthermore, low-complexity robust adaptive beamforming algorithms based on the modified conjugate gradient and an alternating optimisation strategy are proposed. The proposed low-complexity algorithms can compute the existing WC constrained minimum variance and the proposed WC-CCM designs with a quadratic cost in the number of parameters. Simulations show that the proposed WC-CCM algorithm performs better than existing robust beamforming algorithms. Moreover, the numerical results also show that the performances of the proposed low-complexity algorithms are equivalent or better than that of existing robust algorithms, whereas the complexity is more than an order of magnitude lower.
Sparse representation-based feature extraction combined with support vector machine for sense-through-foliage target detection and recognition
- Author(s): Shijun Zhai and Ting Jiang
- Source: IET Signal Processing, Volume 8, Issue 5, p. 458 –466
- DOI: 10.1049/iet-spr.2013.0281
- Type: Article
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Owing to multipath propagation effects of rough surfaces, scattering from trees and ground tend to overwhelm the weak backscattering of targets, which makes it more difficult for sense-through-foliage target detection and recognition. In this study, a novel method to detect and recognise targets obscured by foliage based on sparse representation (SR) and support vector machine (SVM) is proposed. SR theory is applied to analysing the components of received radar signals and sparse coefficients are used to describe target features, the dimension of the sparse coefficients is reduced using principal component analysis (PCA). Then, an improved SVM classifier is developed to perform target detection and recognition. A chaotic differential evolution optimisation approach using tent map is developed to determine the parameters of SVM. The experimental results indicate that the proposed approach is an effective method for sense-through-foliage target detection and recognition, which can achieve higher accuracy than that of the differential evolution-optimised SVM, SVM, k-nearest neighbour and BP neural network (BPNN).
Generalised Kalman filter tracking with multiplicative measurement noise in a wireless sensor network
- Author(s): Xiaoqing Hu ; Yu-Hen Hu ; Bugong Xu
- Source: IET Signal Processing, Volume 8, Issue 5, p. 467 –474
- DOI: 10.1049/iet-spr.2013.0161
- Type: Article
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A new generalised Kalman filtering algorithm using a multiplicative measurement noise model is developed for tracking moving targets in a wireless sensor network. This multiplicative error model facilitates more accurate characterisation of the distance dependence measurement errors of range-estimating sensors. Two new formulations of extended Kalman filter (EKF) and unscented Kalman filter (UKF), called generalised EKF (GEKF) and generalised UKF (GUKF) are derived. Comparing with conventional EKF and UKF formulations, it is shown that GEKF and GUKF can achieve smaller tracking error than traditional EKF and UKF. Simulation results are also reported that demonstrated the superior performance of GEKF and GUKF over existing methods.
Systematic approach in designing wavelet packet modulation-orthogonal frequency division multiplexing radar signal by applying the criterion of least-squares
- Author(s): Mostafa Alimosaymer and Reza Mohseni
- Source: IET Signal Processing, Volume 8, Issue 5, p. 475 –482
- DOI: 10.1049/iet-spr.2013.0228
- Type: Article
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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.
Bias compensation-based recursive least-squares estimation with forgetting factors for output error moving average systems
- Author(s): Ai-Guo Wu ; Yang-Yang Qian ; Wei-Jun Wu
- Source: IET Signal Processing, Volume 8, Issue 5, p. 483 –494
- DOI: 10.1049/iet-spr.2013.0327
- Type: Article
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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.
Design of low-complexity scheme for maintaining distortion-free multi-carrier communications
- Author(s): Keith John Jones
- Source: IET Signal Processing, Volume 8, Issue 5, p. 495 –506
- DOI: 10.1049/iet-spr.2013.0057
- Type: Article
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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.
Robust and rapid converging adaptive beamforming via a subspace method for the signal-plus-interferences covariance matrix estimation
- Author(s): Mostafa Rahmani and Mohammad Hasan Bastani
- Source: IET Signal Processing, Volume 8, Issue 5, p. 507 –520
- DOI: 10.1049/iet-spr.2013.0298
- Type: Article
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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.
Competitive linear parallel interference cancellation detection based on monotone line-search techniques
- Author(s): Abdelouahab Bentrcia and Saleh Alshebeili
- Source: IET Signal Processing, Volume 8, Issue 5, p. 521 –529
- DOI: 10.1049/iet-spr.2013.0096
- Type: Article
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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 large-scale 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.
Fast mode decision scheme using sum of the absolute difference-based Bayesian model for the H.264/AVC video standard
- Author(s): Jongho Kim ; Jong-Hyeok Lee ; Byung-Gyu Kim ; Jin Soo Choi
- Source: IET Signal Processing, Volume 8, Issue 5, p. 530 –539
- DOI: 10.1049/iet-spr.2013.0179
- Type: Article
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H.264/AVC is the most widely used recent video coding standard. It provides a high encoding efficiency but it also has a high computational complexity. The block mode decision for motion estimation is the most time-consuming procedure. A complexity reduction method for the block mode decision procedure is proposed. To reduce the complexity, all block modes are divided into several candidate block mode groups. The sum of the absolute difference (SAD) value, including the motion cost of each mode, is used as a classification feature to divide the block modes into several groups. A refinement method using a Bayesian model based on the average SAD value is also proposed. For B-slices, a differential block mode allocation method is suggested. A different number of candidate modes are allocated for lists (list 0, list 1) based on the SAD value of each list after 16 × 16 block motion estimation. The proposed method achieves an average time-saving for the total encoding time of 65% for IPPP and 66.01% for the hierarchical-B structure.
Matching pursuit for direction of arrival estimation in the presence of Gaussian noise and impulsive noise
- Author(s): Sedigheh Ghofrani
- Source: IET Signal Processing, Volume 8, Issue 5, p. 540 –551
- DOI: 10.1049/iet-spr.2013.0286
- Type: Article
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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.
Sparsity-based space–time adaptive processing using complex-valued Homotopy technique for airborne radar
- Author(s): Zhaocheng Yang ; Xiang Li ; Hongqiang Wang ; Lei Nie
- Source: IET Signal Processing, Volume 8, Issue 5, p. 552 –564
- DOI: 10.1049/iet-spr.2013.0069
- Type: Article
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In this study, a novel sparsity-based space–time adaptive processing algorithm based on the complex-valued Homotopy technique is proposed for airborne radar applications. The proposed algorithm firstly extends the existing standard real-valued Homotopy method to a more general complex-valued application using the gradient approaches. By exploiting the sparsity of the clutter spectrum in the whole spatiotemporal plane, the proposed algorithm recovers the clutter spectrum via the proposed complex Homotopy algorithm and then uses it to estimate the clutter covariance matrix, followed by the space–time filtering and the target detection. Furthermore, the implementations of the proposed algorithm are detailed. The computational complexity analysis shows that the proposed algorithm has a lower-computational complexity than the existing complex-valued Homotopy algorithm. Simulation results show that the proposed algorithm converges at a very fast speed (only 4–6 snapshots in the authors simulations) and provides both excellent detection performance and easy parameter settings.
Faster mode determination algorithm using mode correlation for multi-view video coding
- Author(s): Pei-Jun Lee ; Ho-Ju Lin ; Kuei-Ting Kuo
- Source: IET Signal Processing, Volume 8, Issue 5, p. 565 –578
- DOI: 10.1049/iet-spr.2012.0286
- Type: Article
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Multi-view video coding with a hierarchical B picture structure utilises intra-view and inter-view predictions to reduce the quantity of redundant information. The optimal coding mode is determined by exhaustively searching through all possible partition modes; however, a high degree of computational complexity is involved in such exhaustive searches. In this study, the authors statistically analyse the coding mode distribution in inter-view and intra-view and propose a fast mode decision algorithm to select the optimal mode in terms of rate–distortion optimisation. The probability density function of the rate-distortion cost and the degree of the homogeneity in motion are set as the multi-threshold in the algorithm to determine the optimal mode for base view coding. For the multi-view coding, the correlation of the modes in neighbouring views with similar regions is utilised to select the coding mode from the inter-view or intra-view predictions. The experimental results show that the encoding time for the base view and the multi-view is reduced by up to 85 and 69%, respectively, and the quality of the reconstructed video is nearly unchanged.
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