A method of robust clutter suppression with space–time adaptive processing (STAP) for airborne radar in heterogeneous environments is proposed, which is based on multi frames and the similarity between the cell under test and each training sample. The proposed method deals with the problem of covariance matrix estimation for STAP in heterogeneous clutter. Firstly, the method expands the set of training samples by selecting similar training frames from past frames. Secondly, initial training samples are selected from the expanded training samples set, that are composed of the samples of the current frame and past frames. Thirdly, initial training samples which may be contaminated by target signal are discarded. Fourthly, the similarities between the cell under test and the remaining training samples are estimated, and training samples which are more similar to the cell under test are assigned higher weights in the estimation of the clutter covariance matrix. The proposed method overcomes the problems of training samples’ heterogeneity and insufficiency in the estimation of the clutter covariance matrix. The accuracy of the estimated clutter character is improved significantly, and thus the performance of clutter suppression is improved. Experimental results based on measured data demonstrate the performance of the proposed method.
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Space–time adaptive processing
Cell under test
Independent and identically distributed
Degree of freedom
Coherent process interval
Modified sample matrix inversion
Lan, L., Liao, G., Xu, J., Zhang, Y., & Liao, B. (2020a). Transceive beamforming with accurate nulling in FDA-MIMO radar for imaging. IEEE Transaction on Geoscience and Remote Sensing, 58(6), 4145–4159.
Lan, L., Xu, J., Liao, G., Zhang, Y., Fioranelli, F., & So, H. C. (2020b). Suppression of mainbeam deceptive jammer with FDA-MIMO radar. IEEE Transaction on Vehicular Technology, 69(10), 11584–11598.
Li, R., Li, J., & Zhang, W. (2016). He Z (2016) Reduced-dimension space-time adaptive processing based on angle-Doppler correlation coefficient. EURASIP Journal on Advances in Signal Processing, 1, 97.
Li, Z., Zhang, Y., Guo, Y., Zheng, G., & Zhou, H. (2019). A robust STAP approach for airborne FDA radar with multiple possible prior information constraints. Multidimensional Systems and Signal Processing, 30, 2147–2166. https://doi.org/10.1007/s11045-019-00647-6.
Rangaswamy, M. (2005). Statistical analysis of the nonhomogeneity detector for non-Gaussian interference backgrounds. IEEE Transactions on Signal Processing, 53(6), 2101–2111.
Rangaswamy, M., Michels, J. H., & Himed, B. (2004a). Statistical analysis of the non-homogeneity detector for STAP applications. Digital Signal Processing, 14(3), 253–267.
Rangaswamy, M., Michels, J. H., & Himed, B. (2004b). Statistical analysis of the non-homogeneity detector for stap applications. Digital Signal Processing, 14(3), 253–267.
Riedl, M., & Potter, L. C. (2018). Multi-model shrinkage for knowledge-aided space-time adaptive processing. IEEE Transactions on Aerospace and Electronic Systems, 99, 1–1.
Sun, K., Meng, H., Wang, Y., & Wang, X. (2011). Direct data domain STAP using sparse representation of clutter spectrum. Signal Processing, 91(9), 2222–2236.
Tang, B., Tang, J., & Peng, Y. (2012). Detection of heterogeneous samples based on loaded generalized inner product method. Digital Signal Processing, 22, 605–613.
Wang, P., Li, H., & Himed, B. (2013). A Parametric moving target detector for distributed MIMO radar in non-homogeneous environment. IEEE Transactions on Signal Processing, 61(9), 2282–2294.
Wang, T., Zhao, Y., Chen, S., & Zhang, K. (2017a). A cascaded reduced-dimension STAP method for airborne mimo radar in the presence of jammers. Radio Engineering, 26(1), 337–344.
Wang, Z., Xie, W., Duan, K., & Wang, Y. (2017b). Clutter suppression algorithm based on fast converging sparse Bayesian learning for airborne radar. Signal Processing, 130, 159–168.
Wang, W., Zou, L., Wang, X., & Yang, Y. (2018a). Deterministic-aided single dataset STAP method based on sparse recovery in heterogeneous clutter environments. Journal on Advances in Signal Processing, 2018(1), 24.
Wang, W., Zou, L., Wang, X., & Yang, Y. (2018b). Deterministic-aided single dataset stap method based on sparse recovery in heterogeneous clutter environments. Journal on Advances in Signal Processing, 2018(1), 24.
Wang, W., Zou, L., Wang, X., & Yang, Y. (2018c). Deterministic-aided single dataset STAP method based on sparse recovery in heterogeneous clutter environments. Journal on Advances in Signal Processing, 2018(1), 24.
Wu, Y., Wang, T., Wu, J., & Duan, J. (2015). Training sample selection for space-time adaptive processing in heterogeneous environments. IEEE Letters on Geoscience and Remote Sensing, 12(4), 691–695.
Yang, X., Liu, Y., & Long, T. (2013). Robust non-homogeneity detection algorithm based on prolate spheroidal wave functions for space-time adaptive processing. IET Radar, Sonar & Navigation, 7(1), 47–54.
Yifeng, W., Tong, W., Jianxin, W., & Jia, D. (2015). Robust training samples selection algorithm based on spectral similarity for space–time adaptive processing in heterogeneous interference environments. IET Radar, Sonar & Navigation, 9(7), 778–782.
Zhang, W., He, Z. S., & Li, H. Y. (2018). Linear regression based clutter reconstruction for STAP. IEEE Access, PP(99), 1.
Zhao, X., He, Z., Wang, Y., & Sun, G. (2018). Reduced-dimension STAP using a modified generalised sidelobe canceller for collocated MIMO radars. IET Radar, Sonar & Navigation, 12(12), 1476–1483.
Zhiqi, G., & Haihong, T. (2016). Robust STAP algorithm based on knowledge-aided sparse recovery for airborne radar. IET Radar Sonar & Navigation, 11(2), 321–329.
This study was supported by the China Postdoctoral Science Foundation funded project Under Grant Number 2019M651994 and the Aviation Science Foundation of China Under Grant Number 20172007002, and the Postdoctoral Science Foundation of Jiangsu Province Under Grant Numbers 2018K048C and 2019Z101, as well.
The China Postdoctoral Science Foundation (grant number 2019M651994) and the Postdoctoral Science Foundation of Jiangsu Province (grant numbers 2018K048C and 2019Z101) support the study, and the Aviation Science Foundation (under grant number 20172007002) supports the data of the study.
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Duan, J., Wu, Y., Deng, X. et al. Robust clutter suppression in heterogeneous environments based on multi frames and similarities. Multidim Syst Sign Process (2021). https://doi.org/10.1007/s11045-021-00792-x
- Airborne radar
- Heterogeneous clutter suppression
- Space–time adaptive processing (STAP)
- Covariance matrix estimation