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Persymmetric Two-Step-Based Detectors for Range-Spread Targets against Compound-Gaussian Clutter
Research Article | Published: 03 November 2025
Chinese Journal of Information Fusion Volume 2, Issue 4, 2025: 296-312
Volume 2, Issue 4, Chinese Journal of Information Fusion
Volume 2, Issue 4, 2025
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Hongqi Fan
Hongqi Fan
National University of Defense Technology, China
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Chinese Journal of Information Fusion, Volume 2, Issue 4, 2025: 296-312

Open Access | Research Article | 03 November 2025
Persymmetric Two-Step-Based Detectors for Range-Spread Targets against Compound-Gaussian Clutter
by
Tao Jian Tao Jian 1,2 *
Zikeng Xie Zikeng Xie 1,2
Jia He Jia He 1,2
Lei Guo Lei Guo 3
Xiaoming Tang Xiaoming Tang 4
1 Research Institute of Information Fusion, Naval Aviation University, Yantai 264001, China
2 Key Laboratory of Sea-Air Information Perception and Processing Technology of Shandong Provincial, Yantai 264001, China
3 Unit 92830 of PLA, Haikou 570100, China
4 The 3H Company, Yantai 264001, China
* Corresponding Author: Tao Jian, [email protected]
DOI: 10.62762/CJIF.2025.664545
Received: 27 June 2025, Accepted: 03 September 2025, Published: 03 November 2025  
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Abstract
This research addresses the challenge of detecting radar range-spread targets in compound-Gaussian clutter environments. In such scenarios, the target signals occupy unknown coordinates within a subspace, while the clutter is modeled as compound-Gaussian distribution involving inverse Gamma textures and complex Gaussian speckles with an unknown persymmetric covariance matrix. Additionally, we assume the availability of training data for estimating clutter covariance matrix. Utilizing a two-step approach, three detectors are proposed according to the Gradient, Rao, and Wald tests, by taking advantage of the persymmetry of the clutter covariance matrix. Theoretical analyses demonstrate that the proposed detectors maintain an asymptotically constant false alarm rate relative to the structure of the clutter covariance matrix. Furthermore, Monte Carlo simulations have yielded numerical evidence indicating that the proposed detectors outperform the current contrastive approaches, especially in cases where the training data is scarce.
Graphical Abstract
Persymmetric Two-Step-Based Detectors for Range-Spread Targets against Compound-Gaussian Clutter
Keywords
compound-Gaussian clutter
range-spread targets
persymmetry
gradient test
rao test
wald test
Data Availability Statement
Data will be made available on request.
Funding
This work was supported in part by the National Natural Science Foundation of China under Grant 62471483 and Grant 61971432; in part by the Taishan Scholar Project of Shandong Province under Grant tsqn201909156.
Conflicts of Interest
Xiaoming Tang is an employee of The 3H Company, Yantai 264001, China.
Ethical Approval and Consent to Participate
Not applicable.
References
  1. Liu, W., Liu, J., Hao, C., Gao, Y., & Wang, Y. (2022). Multichannel adaptive signal detection: Basic theory and literature review. Science China Information Sciences, 65(2), 121301.
    [CrossRef]   [Google Scholar]
  2. Jian, T., He, J., Liu, Y., He, Y., Xu, C., & Xie, Z. (2023). Persymmetric adaptive detection of range-spread targets in subspace interference plus Gaussian clutter. Science China Information Sciences, 66(5), 152306.
    [CrossRef]   [Google Scholar]
  3. Tang, P., Dong, R., Liu, W., & Liu, J. (2022). Adaptive multichannel detectors for distributed target based on Gradient test. Signal Processing, 191, 108350.
    [CrossRef]   [Google Scholar]
  4. Ciuonzo, D., De Maio, A., & Orlando, D. (2016). A unifying framework for adaptive radar detection in homogeneous plus structured interference—Part I: On the maximal invariant statistic. IEEE Transactions on Signal Processing, 64(11), 2894–2906.
    [CrossRef]   [Google Scholar]
  5. Ciuonzo, D., De Maio, A., & Orlando, D. (2016). A unifying framework for adaptive radar detection in homogeneous plus structured interference—Part II: Detectors design. IEEE Transactions on Signal Processing, 64(11), 2907–2919.
    [CrossRef]   [Google Scholar]
  6. Liu, W., Xie, W., Liu, J., & Wang, Y. (2014). Adaptive double subspace signal detection in Gaussian background—Part I: Homogeneous environments. IEEE Transactions on Signal Processing, 62(9), 2345–2357.
    [CrossRef]   [Google Scholar]
  7. Ren, Z., Yi, W., Kong, L., & Yang, X. (2023). Adaptive range and doppler distributed target detection in non-Gaussian clutter. IEEE Transactions on Signal Processing, 71, 2376–2390.
    [CrossRef]   [Google Scholar]
  8. Liu, W., Wu, Y., Jiang, Q., & Liu, J. (2025). Eigenvalue-based distributed target detection in compound-Gaussian clutter. Science China Information Sciences.
    [CrossRef]   [Google Scholar]
  9. Shang, X., & Song, H. (2011). Radar detection based on compound-Gaussian model with inverse gamma texture. IET Radar, Sonar & Navigation, 5(3), 315–321.
    [CrossRef]   [Google Scholar]
  10. Shang, X., Song, H., Wang, Y., & He, X. (2012). Adaptive detection of distributed targets in compound-Gaussian clutter with inverse gamma texture. Digital Signal Processing, 22(6), 1024–1030.
    [CrossRef]   [Google Scholar]
  11. Cui, G., Kong, L., Yang, X., & Yang, J. (2012). The Rao and Wald tests designed for distributed targets with polarization MIMO radar in compound-Gaussian clutter. Circuits, Systems, and Signal Processing, 31(1), 237-254.
    [CrossRef]   [Google Scholar]
  12. Chen, S., Kong, L., & Yang, J. (2013). Adaptive detection in compound-Gaussian clutter with inverse Gaussian texture. Progress In Electromagnetics Research M, 28, 157–167. http://dx.doi.org/10.2528/PIERM12121209
    [Google Scholar]
  13. Li, N., Cui, G., Kong, L., & Yang, X. (2014). MIMO radar moving target detection against compound-Gaussian clutter. Circuits, Systems, and Signal Processing, 33(6), 1819-1839.
    [CrossRef]   [Google Scholar]
  14. Mennad, A., Younsi, A., El Korso, M. N., Hamadouche, M., & Breloy, A. (2017). Adaptive detection of range-spread target in compound-Gaussian clutter without secondary data. Digital Signal Processing, 60, 90–98.
    [CrossRef]   [Google Scholar]
  15. Bandiera, F., Orlando, D., & Ricci, G. (2009). CFAR detection strategies for distributed targets under conic constraints. IEEE Transactions on Signal Processing, 57(9), 3305–3316.
    [CrossRef]   [Google Scholar]
  16. Bandiera, F., De Maio, A., & Ricci, G. (2007). Adaptive CFAR radar detection with conic rejection. IEEE Transactions on Signal Processing, 55(6), 2533–2541.
    [CrossRef]   [Google Scholar]
  17. Coluccia, A., & Ricci, G. (2018). A random-signal approach to robust radar detection. In 2018 52nd Annual Conference on Information Sciences and Systems (CISS) (pp. 1–6). IEEE.
    [CrossRef]   [Google Scholar]
  18. Liu, W., Liu, J., Gao, Y., Wang, G., & Wang, Y. L. (2020). Multichannel signal detection in interference and noise when signal mismatch happens. Signal Processing, 166, 107268.
    [CrossRef]   [Google Scholar]
  19. Wang, Z., Liu, W., & Chen, H. (2024). Adaptive detection for multichannel signals in non-ideal environments. CRC Press.
    [Google Scholar]
  20. Sun, M., Liu, W., Liu, J., & Xie, W. (2024). Multiple subspace-based target detection in deterministic interference. IEEE Signal Processing Letters, 31, 3134–3138.
    [CrossRef]   [Google Scholar]
  21. Bandiera, F., De Maio, A., Greco, A. S., & Ricci, G. (2007). Adaptive radar detection of distributed targets in homogeneous and partially homogeneous noise plus subspace interference. IEEE Transactions on Signal Processing, 55(4), 1223–1237.
    [CrossRef]   [Google Scholar]
  22. Liu, W., Liu, J., Huang, L., Wang, Y., & Wang, Y. (2015). Rao tests for distributed target detection in interference and noise. Signal Processing, 117, 333–342.
    [CrossRef]   [Google Scholar]
  23. Liu, W., Liu, J., Li, H., & Wang, Y. (2019). Multichannel signal detection based on Wald test in subspace interference and Gaussian noise. IEEE Transactions on Aerospace and Electronic Systems, 55(3), 1370–1381.
    [CrossRef]   [Google Scholar]
  24. Jiang, Q., Wu, Y., Liu, W., & Liu, J. (2023). Subspace-based distributed target detection in compound-Gaussian clutter. Digital Signal Processing, 140, 104141.
    [CrossRef]   [Google Scholar]
  25. Balleri, A., Nehorai, A., & Wang, J. (2007). Maximum likelihood estimation for compound-Gaussian clutter with inverse gamma texture. IEEE Transactions on Aerospace and Electronic Systems, 43(2), 775–779.
    [CrossRef]   [Google Scholar]
  26. Gao, Y., Ji, H., & Liu, W. (2019). Persymmetric adaptive subspace detectors for range-spread targets. Digital Signal Processing, 89, 116–123.
    [CrossRef]   [Google Scholar]
  27. Liu, J., Jian, T., Liu, W., & Wang, Y. (2020). Persymmetric adaptive detection with improved robustness to steering vector mismatches. Signal Processing, 176, 107669.
    [CrossRef]   [Google Scholar]
  28. Aubry, A., De Maio, A., Foglia, G., & Orlando, D. (2015). Diffuse multipath exploitation for adaptive radar detection. IEEE Transactions on Signal Processing, 63(5), 1268–1281.
    [CrossRef]   [Google Scholar]
  29. Zhang, J., Wang, Z., Zhao, Z., & He, Q. (2020). Persymmetric adaptive detection with reduced-dimension approach. IEEE Signal Processing Letters, 27, 565–569.
    [CrossRef]   [Google Scholar]
  30. Jian, T., Wang, Z., & Wang, H. (2022). Ship target HRRP meta-learning recognition with small samples based on loss weighted correction. Journal of Signal Processing, 38(12), 2460–2468.
    [Google Scholar]
  31. Liao, X., Xie, J., & Zhou, J. (2023). Compound-Gaussian Spatial-Temporal Correlated Complex Clutter Simulation Based on a Two-Step Data-Driven Method. IEEE Transactions on Aerospace and Electronic Systems, 59(6), 9512-9526.
    [CrossRef]   [Google Scholar]
  32. Huang, P., Yang, H., Zou, Z., Xia, X. G., & Liao, G. (2022). Multichannel clutter modeling, analysis, and suppression for missile-borne radar systems. IEEE Transactions on Aerospace and Electronic Systems, 58(4), 3236-3260.
    [CrossRef]   [Google Scholar]
  33. Shi, X., Yang, C., Wang, X., & He, Y. (2023). Dual-Polarimetric persymmetric adaptive subspace detector for range-spread targets in heavy-tailed non-Gaussian clutter. IEEE Geoscience and Remote Sensing Letters, 20, 1–5.
    [CrossRef]   [Google Scholar]
  34. Hao, C., Gazor, S., Foglia, G., & Maio, A. D. (2015). Persymmetric adaptive detection and range estimation of a small target. IEEE Transactions on Aerospace and Electronic Systems, 51(4), 2590–2604.
    [CrossRef]   [Google Scholar]
  35. Wang, Z., Li, M., Chen, H., & Liu, W. (2016). Persymmetric detectors of distributed targets in partially homogeneous disturbance. Signal Processing, 128, 382–388.
    [CrossRef]   [Google Scholar]
  36. Gao, Y., Liao, G., Zhang, S., & Liu, W. (2013). A persymmetric GLRT for adaptive detection in compound-Gaussian clutter with random texture. IEEE Signal Processing Letters, 20(6), 615–618.
    [CrossRef]   [Google Scholar]
  37. Guo, X., Tao, H., Zhao, H. Y., & Liu, Y. (2017). Persymmetric Rao and Wald tests for adaptive detection of distributed targets in compound-Gaussian noise. IETF Radar, Sonar & Navigation, 11(3), 453–458.
    [CrossRef]   [Google Scholar]
  38. Xu, Z., Liu, W., Wu, C., Du, Q., & Liu, J. (2025). Statistical Performance of Generalized Direction Detectors with Known Spatial Steering Vector. IEEE Signal Processing Letters.
    [CrossRef]   [Google Scholar]
  39. Liu, J., Liu, W., Hao, C., & Gao, Y. (2020). Persymmetric subspace detectors with multiple observations in homogeneous environments. IEEE Transactions on Aerospace and Electronic Systems, 56(4), 3276–3284.
    [CrossRef]   [Google Scholar]
  40. Sun, M., Liu, W., Liu, J., & Xie, W. (2021). Adaptive subspace detection based on Gradient test for orthogonal interference. IEEE Transactions on Aerospace and Electronic Systems, 58(3), 1868–1877.
    [CrossRef]   [Google Scholar]
  41. Sun, M., Liu, W., Liu, J., & Hao, C. (2021). Rao and Wald tests for target detection in coherent interference. IEEE Transactions on Aerospace and Electronic Systems, 58(3), 1906-1921.
    [CrossRef]   [Google Scholar]
  42. Van Trees, H. L. (2002). Optimum array processing: Part IV of detection, estimation, and modulation theory. John Wiley & Sons.
    [CrossRef]   [Google Scholar]
  43. Gini, F., & Greco, M. (2002). Covariance matrix estimation for CFAR detection in correlated heavy tailed clutter. Signal Processing, 82(12), 1847–1859.
    [CrossRef]   [Google Scholar]
  44. Liu, J., Gao, Z., Sun, Z., & Wang, Y. (2022). Detection architecture with improved classification capabilities for covariance structures. Digital Signal Processing, 123, 103404.
    [CrossRef]   [Google Scholar]
  45. Conte, E., De Maio, A., & Ricci, G. (2001). GLRT-based adaptive detection algorithms for range-spread targets. IEEE Transactions on Signal Processing, 49(7), 1336–1348.
    [CrossRef]   [Google Scholar]
  46. Wang, Z., Zhao, Z., Ren, C., & He, Q. (2019). Adaptive GLR-, Rao- and Wald-based CFAR detectors for a subspace signal embedded in structured Gaussian interference. Digital Signal Processing, 92, 139–150.
    [CrossRef]   [Google Scholar]
  47. McMaster IPIX radar. (2001). Cognitive Systems Laboratory – McMaster University. (Accessed on 01 November 2025). Archived at: https://web.archive.org/web/20170712063506/https://soma.ece.mcmaster.ca/ipix/
    [Google Scholar]
  48. Harville, D. A. (1998). Matrix algebra from a statistician's perspective. Springer.
    [CrossRef]   [Google Scholar]
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APA Style
Jian, T., Xie, Z., He, J., Guo, L., & Tang, X. (2025). Persymmetric Two-Step-Based Detectors for Range-Spread Targets against Compound-Gaussian Clutter. Chinese Journal of Information Fusion, 2(4), 296–312. https://doi.org/10.62762/CJIF.2025.664545
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