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2020-06-12
Variation of the Shape Parameter of k -Distribution for Sea Clutter with the Spatial Correlation of Sea Surface
By
Progress In Electromagnetics Research Letters, Vol. 92, 25-30, 2020
Abstract
In this study, the physical relationship between the shape parameter v of the K-distribution and the spatial correlation of a sea clutter signal received with a radar is demonstrated through simulation results. The spikiness of the sea clutter is well modeled by the shape parameter v of the K-distribution. According to a well-known empirical formula, the shape parameter v changes with the radar resolution based on a constant power-law relation. However, as with most empirical findings, this finding is valid only for the environmental conditions under which the formula was developed. In other words, the existing power-law models for the shape parameter of the K-distribution for sea clutter do not consider the relative ratio of the cross-range resolution Rc to the spatial decorrelation length Rdec of the sea surface. Our study investigates this relation using statistical simulations based on the principle of superposition for backscattered signals that represent sea clutter within a resolved area on the sea surface. This study shows that the constant factor α in the power-law relation must be modified to a function of the ratio Rc/Rdec. The findings of this study will be useful for the evaluation of detection performance in designing radar systems operating in the maritime environment.
Citation
Jung-Hoon Park, Dong-Hoon Kim, Dong-Hwan Kim, and Sanghoek Kim, "Variation of the Shape Parameter of k -Distribution for Sea Clutter with the Spatial Correlation of Sea Surface," Progress In Electromagnetics Research Letters, Vol. 92, 25-30, 2020.
doi:10.2528/PIERL20042402
References

1. Ward, K. D., R. J. A. Tough, and S. Watts, Sea Clutter: Scattering, the K Distribution and Radar Performance, The Institution of Engineering and Technology, London, 2013.

2. Ward, K. D. and S. Watts, "Radar sea clutter," Microwave Journal, Vol. 28, No. 6, 109-121, 1985.

3. Watts, S. and D. C. Wicks, "Empirical models for detection prediction in K-distribution radar sea clutter," IEEE International Conference on Radar, 189-194, IEEE, 1990.
doi:10.1109/RADAR.1990.201160

4. Rosenberg, L. and S. Watts, "Continuous sea clutter models for the mean backscatter and K-distribution shape," International Conference on Radar Systems (Radar 2017), 2017.

5. Tough, R. J. A. and K. D. Ward, "The correlation properties of gamma and other non-Gaussian processes generated by memoryless nonlinear transformation," Journal of Physics D: Applied Physics, Vol. 32, No. 23, 3075, 1999.
doi:10.1088/0022-3727/32/23/314

6. Bocquet, S., L. Rosenberg, and S. Watts, "Simulation of coherent sea clutter with inverse gamma texture," 2014 International Radar Conference, 1-6, IEEE, 2014.

7. Rosenberg, L., S.Watts, S. Bocquet, and M. Ritchie, "Characterisation of the Ingara HGA dataset," 2015 IEEE Radar Conference (RadarCon), 0027-0032, IEEE, 2015.
doi:10.1109/RADAR.2015.7130965