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Radar, Sonar and Navigation, IEE Proceedings -

Issue 2 • Date Apr 1996

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Displaying Results 1 - 8 of 8
  • Simulations of the positioning accuracy of integrated vehicular navigation systems

    Page(s): 121 - 128
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (740 KB)  

    The positioning performance of vehicular navigation systems based on a differential Global Position System (DGPS), dead-reckoning (DR), map matching (MM) and Kalman filter technologies are simulated. The vehicular navigation system using a 10-state extended Kalman filter to combine DGPS and DR can improve positioning accuracy. When DGPS signal obscuring occurs in the urban environment, DR with a map matching process can supplementally provide an accurate position for the vehicle. The simulation results of the paper can be used as a reference for the design, implementation and field tests of vehicular navigation systems View full abstract»

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  • Performance of neural data associator

    Page(s): 71 - 78
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (708 KB)  

    The paper presents the performance of neural data association based on a mean field Hopfield network. The authors create a new energy function for measurement data association (MDA) that consists of assigning radar plots to predicted track positions which plays a key role in all track-while-scan systems. The network presented in the paper in combination with the new energy function can minimise a global cost, which is a function of the distances between the plots in a given scan of data and the predicted track positions. The data association capacities of the neural network have been studied in different environments, and the results are presented. The authors also give the results of tracking trials based on neural data association View full abstract»

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  • Feature-mapping data fusion

    Page(s): 65 - 70
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (556 KB)  

    A centralised plot-level data fusion technique, which is based on a neural network, is presented. A self-organised feature-mapping technique, which learns from sensor observations, is used to integrate the data from several sensors with various unknown measurement accuracies. Topological neighbourhood formulation among networks for integrating data is described. Computer simulations show that neural data fusion enjoys many advantages over maximum likelihood fusion and other ad hoc fusion methods View full abstract»

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  • Ground clutter model for airborne MPRF radars in look-down search mode

    Page(s): 113 - 120
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1048 KB)  

    The performances of a medium pulse repetition frequency (MPRF) radar operating in air-to-air look-down mode are strongly influenced by the presence of ground clutter. The clutter echo received both on the antenna main beam and side lobes can increase the number of false alarms. To correctly account for the effects of ground clutter, one has to develop a model capable of reproducing clutter characteristics as a function of range and frequency. The authors propose a model to estimate range/Doppler distribution of ground clutter mean power. The proposed discrete model permits an easy development of simulation packages which quantify the effects of ground clutter in MPRF systems and which allow the evaluation of MPRF radars performances in most typical operating conditions. Three-dimensional plots of the range/Doppler distribution of mean ground clutter power, obtained by a FORTRAN code that implements the model, are presented. Moreover, the dependence of ground clutter power on important mission and radar parameters is discussed View full abstract»

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  • Detection of coherent radar signals with unknown Doppler shift

    Page(s): 79 - 86
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (652 KB)  

    The problem of detection of coherent radar signals with unknown Doppler shift in noise plus clutter, which is assumed to be Gaussian is addressed. The authors solve the detection problem and find the optimum ALR detector. Since it includes a difficult integral, they derive several suboptimum detectors including CGLRI AALR and CALR. Comparison of these detectors via computer simulations shows that CALR and CGLR can have near-optimum performance whereas the common radar-detectors may have severe losses View full abstract»

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  • Performance of excision GO-CFAR detectors in nonhomogeneous environments

    Page(s): 105 - 112
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (708 KB)  

    The authors analyse a new CFAR detector which is composed of an excision processor and a greatest-of (GO)-CFAR detector. The new detector is named an excision GO(EXGO)-CFAR detector. Performance of EXGO-CFARs is derived and compared with the existing excision CA(EXCA)-CFAR detectors for Swerling I target model in homogeneous and nonhomogeneous noise environments such as multiple interferers and/or clutter results show that EXGO-CFAR detectors considerably reduce the problem of excessive false alarm probability near clutter edges while maintaining good performance in other environments View full abstract»

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  • Optimum CFAR detection against compound Gaussian clutter with partially correlated texture

    Page(s): 95 - 104
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1092 KB)  

    The paper deals with CFAR detection in compound Gaussian clutter with a partially correlated texture component. A theoretical high performance upgrade has been demonstrated using the ideal exact knowledge of this component in the CFAR scheme (`ideal CFAR') in a paper by Watts (1985) for K-distribution. For practical application the authors derive some optimum local texture estimators, based on the closest range cells: and use the estimated values to set the detection threshold. The schemes differ for operating over the intensity or the logarithm and for using or not prior information about the texture correlation. In particular, a maximum a posteriori estimator is derived, which outperforms the usual cell averaging CFAR and provides always performance close to the `ideal CFAR'. The derivations are valid for all compound Gaussian clutters. The performances obtained with K and compound weibull distribution are compared, assessing the robustness of the proposed detection scheme View full abstract»

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  • Monopulse detection analysis of the trimmed mean CFAR processor in nonhomogeneous situations

    Page(s): 87 - 94
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (676 KB)  

    The false alarm regulation capabilities and the detection performance of a CFAR processor depend on the robustness of the noise level estimation. The well known cell-averaging (CA) CFAR detector exhibits severe performance degradation in the presence of an interfering target return in the reference window or in regions of abrupt change in background clutter power. The ordered statistics (OS) CFAR processor designed to alleviate these problems resolves multiple targets quite well, but it lacks effectiveness in preventing excessive false alarms during clutter power transitions. A modified OS-CFAR detector, known as the trimmed mean (TM) CFAR processor, has been shown to give a marginal improvement in the false alarm rate performance in the presence of clutter edges. The CA- and OS-CFAR schemes are special cases of the TM-CFAR scheme. The detection performance of this processor, in nonhomogeneous environments, was previously studied by means of computer simulation. The author provides a complete detection analysis, for a Swerling II target fluctuation model, for the TM-CFAR processor when more than one target return is present within the reference window and in clutter power transitions 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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