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Radar, Sonar and Navigation, IEE Proceedings -

Issue 2 • Date Apr 1997

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Displaying Results 1 - 7 of 7
  • Statistical analysis of the eigenvector projection method for adaptive spatial filtering of interference

    Page(s): 57 - 63
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (716 KB)  

    Adaptive beamforming based on eigenanalysis of the covariance matrix of the outputs of an array of sensors has been shown to be most effective for removing directional interference. The author provides an analytical performance evaluation of the signal-to-noise-plus-interference ratio (SNIR) of the adaptive beamformer obtained by the eigenvector projection (EVP) method. The approach presented to derive the statistical properties is based only on the statistics of the sensor outputs, rather than on the asymptotical distribution of the eigenvectors of the sample covariance matrix. The expression for the probability density function can be used to predict the achievable rate of convergence and the resulting loss of performance for arbitrary sample sizes and interference environments, particularly when compared to the classical Wiener filter solutions or other suboptimum projection methods. In the case of an infinite interference-to-noise ratio (INR), it is shown that the SNIR is exactly beta-distributed and independent of the number of sensor elements. Furthermore, the case of finite INR is discussed and it is shown that the distribution is valid for nearly all practical cases. Finally some relevant numerical examples are presented to illustrate the agreement with the theoretical results View full abstract»

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  • CFAR detection for Weibull and other log-log-linear tail clutter distributions

    Page(s): 64 - 70
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (692 KB)  

    The problem of CFAR (constant false alarm rate) radar target detection in non-Rayleigh, two-parameter, unknown clutter is already a classical one. The authors describe and evaluate a practical way of solving the already old problem of target detection in two-parameter clutter. By reformulating some basic tail extrapolation theory results, they fully develop a complete detection architecture for Weibull clutter. The method can be directly applied to deal with any other type of clutter of the log-log-linear tail family View full abstract»

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  • 3-D target feature extraction via interferometric SAR

    Page(s): 71 - 80
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1044 KB)  

    The authors consider 3-D (three-dimensional) target feature extraction via an interferometric synthetic aperture radar (IFSAR). The targets of interest are relatively small and consist of a small number of distinct point scatterers. Since using IFSAR to extract the features of such targets has not been addressed before, a self-contained detailed derivation of the data model is presented. A set of sufficient parameter identifiability conditions on the data model and the Cramer-Rao bounds (CRBs) on the parameter estimates are also derived. Four existing two-dimensional feature extraction methods (FFT, windowed FFT, Capon and MUSIC) are extended to estimate the 3-D parameters of the target scatterers. A new nonlinear least squares parameter estimation method, referred to as IFRELAX, is also derived to extract the target features. Finally, numerical examples are presented to compare the performances of the presented methods with each other and with the corresponding CRBs. The authors show by means of numerical examples that, among the three nonparametric methods (FFT, windowed FFT, and Capon), Capon has the best resolution. The parametric methods MUSIC and IFRELAX can have much better resolution and provide much more accurate parameter estimates than the nonparametric methods. It is shown IFRELAX can be faster and provide much better parameter estimates than MUSIC View full abstract»

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  • Detecting anisotropic scattering with hidden Markov models

    Page(s): 81 - 86
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (680 KB)  

    The authors propose that hidden Markov models (HMMs) and multiaperture synthetic aperture radar (MASAR) imaging can be used to construct automatic target detection algorithms. Their preliminary studies show that HMM MASAR detection: effectively exploits anisotropic scattering differences between target and multiple clutter classes; is computationally efficient; can be used with either single polarisation or multipolarisation SAR imagery; and can be used with either coherent or noncoherent subapertures. Further, the results indicate that the accuracy of HMM MASAR detection is comparable to other techniques while requiring orders of magnitude less computation. These results suggest that the HMM MASAR detection technique could be effectively deployed in fielded automatic target recognition systems View full abstract»

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  • Target angular motion effects on ISAR imaging

    Page(s): 87 - 95
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (856 KB)  

    The authors investigate the effects of target roll, pitch and yaw motions in ISAR image reconstruction. They determine the impulse response of an imaging system in the presence of angular motions and evaluate the conditions under which pitch, roll and yaw have negligible effect on image focus. The theoretical analysis is finally validated by presenting and discussing some simulation results View full abstract»

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  • Interferometric synthetic aperture sonar for high resolution 3-D mapping of the seabed

    Page(s): 96 - 103
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1032 KB)  

    A technique is described and demonstrated for generating high-resolution 3-D images of the objects on the seabed, combining synthetic aperture sonar with bathymetric (interferometric) processing. This has applications in surveying of oil wells, pipelines and shipwrecks, and in defence in mine hunting. It is shown that the technique is very sensitive to platform motion errors, and ways are discussed by which the errors may be estimated and compensated. Practical results obtained with an experimental system in a test tank are presented and discussed, and a number of areas for further work are identified View full abstract»

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  • IMMJPDA versus MHT and Kalman filter with NN correlation: performance comparison

    Page(s): 49 - 56
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (744 KB)  

    In a tracking problem a radar periodically scans the volume under surveillance and provides detections (plots) that indicate a target presence. Multitarget tracking systems in operational use today generally adopt Kalman filter (KF) techniques (coupled with a manoeuvre detector to introduce some kind of adaptivity), and nearest neighbour (NN) correlation. Today there are two new approaches to the tracking problem, namely: interacting multiple model joint probabilistic data association (IMMJPDA) and multiple hypothesis tracking (MHT) which promise improved tracking performance. The paper provides a performance comparison between these three tracking algorithms in terms of track maintenance probability and tracking errors. The NN + KF algorithm is used as reference because of its widespread use. Results show that MHT is superior to IMMJPDA and, as expected, both perform better than NN + KF; the cost of additional performance is increased, yet feasible, computing power View full abstract»

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Aims & Scope

IEE Proceedings Radar, Sonar and Navigation covers the theory and practice of systems involving the processing of signals for radar, radio location, radio navigation and surveillance purposes.

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