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Signal Processing, IET

Issue 5 • Date July 2012

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Displaying Results 1 - 16 of 16
  • Editorial - Multi-sensor signal processing for defence: detection, localisation & classification

    Page(s): 393 - 394
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (76 KB)  

    Multi-sensor signal processing is fundamental not only to civilian applications but also to the capability of many defence systems. This special issue presents a collection of research papers that address a number of "hot topics" broadly concerned with the development of new multisensor signal processing algorithms. These algorithms aim to exploit the benefits offered by multi-sensor systems, while addressing the issues resulting from military sensor signals which provide both excellent opportunities and significant research challenges. View full abstract»

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  • Direction and polarisation estimation using polarised cylindrical conformal arrays

    Page(s): 395 - 403
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (648 KB)  

    In this study, the authors describe two-dimensional direction finding and signal polarisation estimation from a cylindrical conformal array consisting of directional and polarised antenna elements. Firstly, a simple and general transformation procedure, based on the mathematical framework of geometric algebra, is presented for arbitrary conformal arrays with polarised and directional antennas. Subsequently, the authors utilise the symmetry of cylindrical arrays to estimate signal parameters via rotational invariance techniques. The authors show how to iteratively estimate the azimuth and elevation angles of the incident signal, as well as its polarisation. To illustrate the versatility of this method, the results of simulations on a 3×4 cylindrical conformal array are shown and discussed. View full abstract»

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  • Generalised parametric rao test for multi-channel adaptive detection of range-spread targets

    Page(s): 404 - 412
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (457 KB)  

    This study considers the problem of detecting a multi-channel signal of range-spread targets in a homogeneous environment, where the disturbances in both test signal and training signals share the same covariance matrix. To this end, a generalised parametric Rao (GP-Rao) test is developed by modelling the disturbance as a multi-channel auto-regressive process. The GP-Rao test uses less training data and is computationally more efficient, when compared with conventional covariance matrix-based solutions. The theoretical detection performance of the GP-Rao test is characterised in terms of the asymptotic distribution under both hypotheses. Numerical results indicate that the proposed GP-Rao test attains asymptotically the constant false alarm rate property. Numerical results show that the GP-Rao test achieves better detection performance and uses significantly less training signals than the covariance matrix-based approach. View full abstract»

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  • Sparsity-aware space-time adaptive processing algorithms with L1-norm regularisation for airborne radar

    Page(s): 413 - 423
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (900 KB)  

    This study proposes novel sparsity-aware space-time adaptive processing (SA-STAP) algorithms with L1-norm regularisation for airborne phased-array radar applications. The proposed SA-STAP algorithms suppose that a number of samples of the full-rank STAP datacube are not meaningful for processing and the optimal full-rank STAP filter weight vector is sparse, or nearly sparse. The core idea of the proposed method is imposing a sparse regularisation (L1-norm type) to the minimum variance STAP cost function. Under some reasonable assumptions, the authors firstly propose an L1-based sample matrix inversion to compute the optimal filter weight vector. However, it is impractical because of its matrix inversion, which requires a high computational cost when using a large phased-array antenna. In order to compute the STAP parameters in a cost-effective way, the authors devise low-complexity algorithms based on conjugate gradient techniques. A computational complexity comparison with the existing algorithms and an analysis of the proposed algorithms are conducted. Simulation results with both simulated and the Mountain-Top data demonstrate that fast signal-to-interference-plus-noise-ratio convergence and good performance of the proposed algorithms are achieved. View full abstract»

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  • Indication of slowly moving ground targets in non-gaussian clutter using multi-channel synthetic aperture radar

    Page(s): 424 - 434
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (544 KB)  

    The problem of how best to maximise the ratio of mean target intensity to mean background intensity for slowly moving targets in sets of multi-channel synthetic aperture radar images is discussed for highly non-Gaussian background clutter. The problem is formulated as a direct maximisation of target-to-clutter ratio thus giving a true maximisation of that ratio. Complex-valued weights derived using generalised eigensystem theory are used to maximise the ratio of quadratic forms representing the mean intensity of the target and background derived from their coherence matrices. For two to four channels it is shown that when the target is highly coherent an optimum steering vector is a discrete Fourier transform. For more than two channels it is shown that the optimal solution is only valid within a subspace of the whole parameter space defined by the correlation parameters of the background clutter. Images from a publically released ground moving target indicator dataset are filtered using the results for three channels. The method outperform a standard space-time adaptive processing algorithm in suppressing the stationary background urban clutter image intensity relative to the image intensity because of a known slowly moving ground vehicle. Moreover, the steering vector is much simpler to implement. View full abstract»

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  • Kalman filtering for widely linear complex and quaternion valued bearings only tracking

    Page(s): 435 - 445
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (314 KB)  

    Bearings only target tracking is concerned with estimating the trajectory of an object from noise-corrupted bearing (phase) measurements. Traditionally this problem has been formulated as real valued for the Cartesian coordinate system or modified polar coordinate system. In this study, the authors introduce the bearings only tracking problem for the complex and quaternion domains to take advantage of the natural representation offered by these domains, for multivariate real signals, as well as the greater insights provided into the dynamics of tracking. Moreover, the authors introduce the augmented complex and quaternion extended Kalman filters for the modelling of second-order non-circular complex and quaternion valued signals, for which a widely linear model is shown to be more suitable than a strictly linear model. View full abstract»

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  • Efficient multisensor fusion with sliding window Kalman filtering for discrete-time uncertain systems with delays

    Page(s): 446 - 455
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (272 KB)  

    In this study, we provide two computationally effective multisensory fusion filtering algorithms for discrete-time linear uncertain systems with state and observation time-delays. The first algorithm is shaped by algebraic forms for multirate sensor systems, and then we propose a matrix form of filtering equations using block matrices. The second algorithm is based on exact cross-covariance matrix equations. These equations are useful to compute matrix weights for fusion estimation in a multidimensional-multisensor environment. Furthermore, our proposed filtering algorithms are based on the sliding window strategy in order to achieve high estimation accuracy and stability under parametric uncertainties. The authors demonstrate the low computational complexities of the proposed fusion filtering algorithms and how the proposed algorithms robust against dynamic model uncertainties comparing with Kalman filtering with time delays. View full abstract»

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  • Maximum likelihood array calibration using particle swarm optimisation

    Page(s): 456 - 465
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (677 KB)  

    Calibration of array shape error is a key issue for most existing source localisation algorithms. In this study, the far-field self-calibration and near-field pilot-calibration are carried out using unconditional maximum likelihood (UML) estimator whose objective function is optimised by particle swarm optimisation (PSO). A new technique, decaying diagonal loading (DDL), is proposed to enhance the performance of PSO at high signal-to-noise ratio (SNR) by dynamically lowering it, based on the counter-intuitive observation that the global optimum of the UML objective function is more prominent at lower SNR. Numerical simulations demonstrate that the UML estimator optimised by PSO with DDL is robust to large shape errors, optimally accurate and free of the initialisation problem. In addition, the DDL technique can be coupled with different global optimisation algorithms for performance enhancement. Mathematical analysis indicates that the DDL is applicable to any array processing problem where the UML estimator is employed. View full abstract»

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  • Multimodal (audiovisual) source separation exploiting multi-speaker tracking, robust beamforming and time-frequency masking

    Page(s): 466 - 477
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1163 KB)  

    A novel multimodal source separation approach is proposed for physically moving and stationary sources which exploits a circular microphone array, multiple video cameras, robust spatial beamforming and time-frequency masking. The challenge of separating moving sources, including higher reverberation time (RT) even for physically stationary sources, is that the mixing filters are time varying; as such the unmixing filters should also be time varying but these are difficult to determine from only audio measurements. Therefore in the proposed approach, visual modality is used to facilitate the separation for both stationary and moving sources. The movement of the sources is detected by a three-dimensional tracker based on a Markov Chain Monte Carlo particle filter. The audio separation is performed by a robust least squares frequency invariant data-independent beamformer. The uncertainties in source localisation and direction of arrival information obtained from the 3D video-based tracker are controlled by using a convex optimisation approach in the beamformer design. In the final stage, the separated audio sources are further enhanced by applying a binary time-frequency masking technique in the cepstral domain. Experimental results show that using the visual modality, the proposed algorithm cannot only achieve performance better than conventional frequency-domain source separations algorithms, but also provide acceptable separation performance for moving sources. View full abstract»

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  • Performance of methods based on the fractional fourier transform for the detection of linear frequency modulated signals

    Page(s): 478 - 483
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (141 KB)  

    Analysis of detectors of linear frequency modulated (LFM) signals based on the fractional Fourier transform (FrFT) is presented. This allows one to conduct a fair comparison of the performance of these methods with those based on the Fourier transform (FT). In order to facilitate this analysis, expressions for the distribution of the coefficients of the FrFT are presented. Analytic approximations for the FT of the LFM signals are also developed. It is shown that the FrFT methods achieve superior performance if the sweep rate is sufficiently fast or the data length is sufficiently large. View full abstract»

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  • Fractal dimension, wavelet shrinkage and anomaly detection for mine hunting

    Page(s): 484 - 493
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1050 KB)  

    An anomaly detection approach is considered for the mine hunting in sonar imagery problem. The authors exploit previous work that used dual-tree wavelets and fractal dimension to adaptively suppress sand ripples and a matched filter as an initial detector. Here, lacunarity inspired features are extracted from the remaining false positives, again using dual-tree wavelets. A one-class support vector machine is then used to learn a decision boundary, based only on these false positives. The approach exploits the large quantities of `normal` natural background data available but avoids the difficult requirement of collecting examples of targets in order to train a classifier. View full abstract»

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  • Target classification of ISAR images based on feature space optimisation of local non-negative matrix factorisation

    Page(s): 494 - 502
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (638 KB)  

    The problem of target classification using inverse synthetic aperture radar (ISAR) images is studied under conditions of mass data processing, sparse scattering centre distribution, image deterioration and variation with the radar imaging view, all of which make target classification difficult. In this study, the authors propose a novel method based on combination of the feature space and the visual perception theory to achieve an accurate and robust classification of ISAR images. In order to make full use of local spatial structure information for classification, the local non-negative matrix factorisation (LNMF) is employed to construct an initial feature space, which is then optimised to calculate more discriminable feature projection vectors of each target. The approaches including speckle noise and stripes suppression, centroid and scale normalisation, LNMF, feature space optimisation with the maximum intersubject variation and minimum intrasubject variation and feature projection vectors calculation are detailed. Finally, the classification is performed with a k neighbours classifier. ISAR images used are obtained by range-Doppler imaging method with radar echoes of aircraft models generated by RadBase. Simulation results show a significant improvement on recognition accuracy and robustness of the proposed method. View full abstract»

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  • Range Doppler and chirp scaling processing of synthetic aperture radar data using the fractional Fourier transform

    Page(s): 503 - 510
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (476 KB)  

    Synthetic aperture radar (SAR) systems are used to form high-resolution images from radar backscatter signals. The fractional Fourier transform (FrFT), which is a generalised form of the well-known Fourier transform, has opened up the possibility of a new range of potentially promising and useful applications that involve the use and detection of chirp signals that include pattern recognition and SAR imaging. In this study a time variant problem associated with the use of the FrFT for SAR processing is addressed and a new algorithm is presented that resolves this problem. Two new FrFT-based SAR processing algorithms are presented namely the FrRDA and the eFrCSA that are shown to improve the well-established range-Doppler and chirp-scaling algorithms for SAR processing. The performance of the algorithms are assessed using simulated and real Radarsat-1 data sets. The results confirm that the FrFT-based SAR processing methods provide enhanced resolution yielding both lower side lobes effects and improved target detection. View full abstract»

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  • Advanced image formation and processing of partial synthetic aperture radar data

    Page(s): 511 - 520
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1453 KB)  

    The authors propose an advanced synthetic aperture radar (SAR) image formation framework based on iterative inversion algorithms that approximately solve a regularised least squares problem. The framework provides improved image reconstructions, compared to the standard methods, in certain imaging scenarios, for example when the SAR data are under-sampled. Iterative algorithms also allow prior information to be used to solve additional problems such as the correction of unknown phase errors in the SAR data. However, for an iterative inversion framework to be feasible, fast algorithms for the generative model and its adjoint must be available. The authors demonstrate how fast, N2 log2N complexity, (re/back)-projection algorithms can be used as accurate approximations for the generative model and its adjoint, without the limiting geometric approximations of other N2 log2N methods, for example, the polar format algorithm. Experimental results demonstrate the effectiveness of their framework using publicly available SAR datasets. View full abstract»

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  • Unsupervised video anomaly detection using feature clustering

    Page(s): 521 - 533
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1102 KB)  

    This study addresses the problem of automatic anomaly detection for surveillance applications. A general framework for anomalous event detection in uncrowded scenes has been developed which consists of the following key components: (i) an efficient foreground detection model based on a Gaussian mixture model (GMM), which can selectively update pixel information in each image region; (ii) an adaptive foreground object tracker that combines the merits of Kalman, mean-shift and particle filtering; (iii) a feature clustering algorithm, which can automatically choose the optimal number of clusters in the training data for scene pattern modelling; (iv) a statistical scene modeller based on Bayesian theory and GMM, which combines trajectory-based and region-based information for enhanced anomaly detection. The resulting approach achieves fully unsupervised anomaly detection in surveillance video. The experimental results show improved detection performance compared with the state-of-the-art methods. View full abstract»

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  • Mean shift tracking through scale and occlusion

    Page(s): 534 - 540
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (523 KB)  

    This study describes a method for tracking objects through scale and occlusion. The technique presented is based on the mean shift algorithm, which provides an efficient way to track objects based on their colour characteristics. A novel and efficient method is derived for tracking through changes in the target scale, where an object of interest moves away or towards the camera and therefore appears to change size in the image plane. The method works by interleaving spatial mean shift iterations with scale iterations. It is shown that this method is considerably more efficient than other methods and possesses other advantages too. It is also demonstrated that the Bhattacharyya coefficient, a histogram similarity metric that is used in the mean shift framework, can be used to reliably detect when target occlusion occurs. In such situations, the motion of an object can be extrapolated to give an accurate estimate of its position. This is used as the basis of a technique for tracking through occlusion. Experimental results are presented on data from various scenarios. View full abstract»

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