Adaptive Rao Detection of Radar Targets Based on the Priori-Knowledge of Sea Clutter

XUE Jian; ZHU Yuanling; PAN Meiyan

doi:10.11999/JEIT221216

Volume 45 Issue 11

Nov. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(11): 3839-3847

XUE Jian, ZHU Yuanling, PAN Meiyan. Adaptive Rao Detection of Radar Targets Based on the Priori-Knowledge of Sea Clutter[J]. Journal of Electronics & Information Technology, 2023, 45(11): 3839-3847. doi: 10.11999/JEIT221216

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XUE Jian, ZHU Yuanling, PAN Meiyan. Adaptive Rao Detection of Radar Targets Based on the Priori-Knowledge of Sea Clutter[J]. Journal of Electronics & Information Technology, 2023, 45(11): 3839-3847. doi: 10.11999/JEIT221216

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Adaptive Rao Detection of Radar Targets Based on the Priori-Knowledge of Sea Clutter

doi: 10.11999/JEIT221216 cstr: 32379.14.JEIT221216

1.
School of Communications and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
2.
Xi'an Electronic Engineering Research Institute, Xi’an 710100, China

Funds: The National Natural Science Foundation of China (62201455), The Scientific Research Program Funded by Shaanxi Provincial Education Department (22JK0566)

Received Date: 2022-09-19
Rev Recd Date: 2022-10-30

Available Online: 2022-11-03

Publish Date: 2023-11-28

Abstract

Abstract

An adaptive Rao detection method for radar targets is proposed based on the priori knowledge of sea clutter to improve the radar’s target detection performance in non-Gaussian and nonhomogeneous sea clutter. First, the texture component and the speckle covariance matrix of sea clutter are modeled as an inverse Gaussian random variable and an inverse complex Wishart random matrix, respectively. Then, an adaptive Rao detection method for radar targets, with quite similar characteristics as sea clutter, is designed based on the Rao test and unknown parameter estimation. The detection method is verified by theoretical derivation and experiments in demonstrating constant false alarm characteristics for the mean power and covariance mean matrix of sea clutter. The experimental results of the simulated and experimental data reveal that the proposed detection method outperforms existing detection methods in non-Gaussian and nonhomogeneous sea clutter environments with good robustness.
- Radar targets,
- Adaptive Rao detection,
- Sea clutter,
- Priori knowledge

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References(24)

References

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