Search search
Publication Date
to
Vol. 84
Latest Volume
All Volumes
PIERL 131 PIERL 130 PIERL 129 PIERL 128 PIERL 127 PIERL 126 PIERL 125 PIERL 124 PIERL 123 PIERL 122 PIERL 121 PIERL 120 PIERL 119 PIERL 118 PIERL 117 PIERL 116 PIERL 115 PIERL 114 PIERL 113 PIERL 112 PIERL 111 PIERL 110 PIERL 109 PIERL 108 PIERL 107 PIERL 106 PIERL 105 PIERL 104 PIERL 103 PIERL 102 PIERL 101 PIERL 100 PIERL 99 PIERL 98 PIERL 97 PIERL 96 PIERL 95 PIERL 94 PIERL 93 PIERL 92 PIERL 91 PIERL 90 PIERL 89 PIERL 88 PIERL 87 PIERL 86 PIERL 85 PIERL 84 PIERL 83 PIERL 82 PIERL 81 PIERL 80 PIERL 79 PIERL 78 PIERL 77 PIERL 76 PIERL 75 PIERL 74 PIERL 73 PIERL 72 PIERL 71 PIERL 70 PIERL 69 PIERL 68 PIERL 67 PIERL 66 PIERL 65 PIERL 64 PIERL 63 PIERL 62 PIERL 61 PIERL 60 PIERL 59 PIERL 58 PIERL 57 PIERL 56 PIERL 55 PIERL 54 PIERL 53 PIERL 52 PIERL 51 PIERL 50 PIERL 49 PIERL 48 PIERL 47 PIERL 46 PIERL 45 PIERL 44 PIERL 43 PIERL 42 PIERL 41 PIERL 40 PIERL 39 PIERL 38 PIERL 37 PIERL 36 PIERL 35 PIERL 34 PIERL 33 PIERL 32 PIERL 31 PIERL 30 PIERL 29 PIERL 28 PIERL 27 PIERL 26 PIERL 25 PIERL 24 PIERL 23 PIERL 22 PIERL 21 PIERL 20 PIERL 19 PIERL 18 PIERL 17 PIERL 16 PIERL 15 PIERL 14 PIERL 13 PIERL 12 PIERL 11 PIERL 10 PIERL 9 PIERL 8 PIERL 7 PIERL 6 PIERL 5 PIERL 4 PIERL 3 PIERL 2 PIERL 1
All Journals
PIER PIER B PIER C PIER M PIER Letters PIER Photonics
2019-05-27
Distribution of the Cell Under Test in Sliding Window Detection Processes
By
Graham V. Weinberg

    Dr. Graham V. Weinberg

    Weapons and Combat Systems Division

    Defence Science and Technology Group

    Australia

    Homepage

Progress In Electromagnetics Research Letters, Vol. 84, 75-81, 2019
doi:10.2528/PIERL19031501 | Google Scholar

Abstract
Radar sliding window detection processes are often used in signal processing as alternatives to Neyman-Pearson based decision rules, due to the fact that they have a simpler receiver implementation and can often be designed to maintain a constant false alarm rate in homogeneous clutter. These detection processes produce a measurement of the clutter level from a series of observations, and compare a normalised version of this to a cell under test. The latter is an amplitude squared measurement of the signal plus clutter in the complex domain. It has been suggested by some authors that that there is sufficient merit in the approximation of the cell under test by a distributional model similar to that assumed for the clutter distribution. This is certainly the case when a Gaussian target is combined with Gaussian clutter, or equivalently a Swerling 1 target and exponentially distributed intensity clutter. The purpose of the current paper is to demonstrate, in a modern maritime surveillance radar context where the clutter is modelled by Pareto statistics, that such an approximation is only valid under certain limiting conditions.
Download Full Article (770) View Full Article volume
Citation
Graham V. Weinberg, "Distribution of the Cell Under Test in Sliding Window Detection Processes," Progress In Electromagnetics Research Letters, Vol. 84, 75-81, 2019.
doi:10.2528/PIERL19031501
References

1. Finn, H. M. and R. S. Johnson, "Adaptive detection model with threshold control as a function of spatially sampled clutter-level estimate," RCA Review, Vol. 29, 414-464, 1968.        Google Scholar

2. Gandhi, P. P. and S. A. Kassam, "Analysis of CFAR processors in nonhomogeneous background," IEEE Transactions on Aerospace and Electronic Systems, Vol. 24, 427-445, 1988.
doi:10.1109/7.7185        Google Scholar

3. Minkler, G. and J. Minkler, CFAR: The Principles of Automatic Radar Detection in Clutter, Magellan, 1990.

4. Balleri, A., A. Nehorai, and J. Wang, "Maximum likelihood estimation for compound-Gaussian clutter with inverse-Gamma texture," IEEE Transactions on Aerospace and Electronic Systems, Vol. 43, 775-779, 2007.
doi:10.1109/TAES.2007.4285370        Google Scholar

5. arshchian, M. and F. L. Posner, "The Pareto distribution for low grazing angle and high resolution X-band sea clutter," IEEE Radar Conference Proceedings, 789-793, 2010.        Google Scholar

6. Weinberg, G. V., "Assessing Pareto fit to high-resolution high-grazing-angle sea clutter," Electronics Letters, Vol. 47, 516-517, 2011.
doi:10.1049/el.2011.0518        Google Scholar

7. Weinberg, G. V., Radar Detection Theory of Sliding Window Processes, CRC Press, 2017.
doi:10.1201/9781315154015

8. Siddiq, K. and M. Irshad, "Analysis of the cell averaging CFAR in Weibull background using a distributional approximation," 2nd International Conference on Computer, Control and Communication, 2009.        Google Scholar

9. Dong, Y., "Distribution of X-band high resolution and high grazing angle sea clutter," Defence Science and Technology Organisation Research Report, 2006.        Google Scholar

10. Persson, B., "Radar target modeling using in-flight radar cross section measurements," Journal of Aircraft, Vol. 54, 284-291, 2017.
doi:10.2514/1.C033932        Google Scholar

11. Weinberg, G. V., "Constant false alarm rate detectors for pareto clutter models," IET Radar, Sonar and Navigation, Vol. 7, 153-163, 2013.
doi:10.1049/iet-rsn.2011.0374        Google Scholar

12. Bartle, R. G., The Elements of Integration and Lebesgue Measure, Wiley, New York, 1995.
doi:10.1002/9781118164471

13. Kaplan, W., Advanced Calculus, Addison-Wesley, Massachusetts, 1984.

14. Weinberg, G. V., "Validity of whitening-matched filter approximation to the Pareto coherent detector," IET Signal Processing, Vol. 6, 546-550, 2012.
doi:10.1049/iet-spr.2011.0304        Google Scholar

Download Full Article (770) View Full Article volume


The EM Academy piers.org jpier.org Who's Who in EM
Copyright © 2022 The Electromagnetics Academy. All Rights Reserved.