volume | PIER Journals

2019-05-27

Distribution of the Cell Under Test in Sliding Window Detection Processes

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

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.

Citation

Copy Export

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.

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

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

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.

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

7. Weinberg, G. V., Radar Detection Theory of Sliding Window Processes, CRC Press, New York, 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.

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

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

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

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