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<p>Detecting weak radar targets in complex cluttered environments remains a significant challenge, particularly when attempting to effectively detect low signal-to-clutter ratio (SCR) targets while maintaining a constant false alarm rate (CFAR). We propose novel CFAR detectors based on time series analysis and statistical foundations. We model radar echo data within a coherent processing interval as stationary time series governed by linear random processes, enabling the application of a time series resampling approach to establish the autoregressive sieve bootstrap consistency of the banded sample autocovariance matrix (SACM) in the spectral norm. Leveraging this, we derive the numerical distribution of statistics related to the largest eigenvalue of the banded SACM. We introduce two improved CFAR detectors: one based on the banded SACM spectral norm (BSN detector) and another based on the likelihood ratio test in banded SACM eigenvalues (BLR detector). Additionally, we propose an adaptive CFAR detector, the maximum eigenvalue trimmed (MET) detector, developed using single-sample hypothesis testing. Our analysis demonstrates that detection probabilities stabilize as the number of bands exceeds a certain threshold, with robust performance under varying SCRs and false alarm probabilities. Simulations and real data experiments validate that all three detectors significantly outperform traditional radar target detection methods in terms of both detection performance and computational efficiency. Notably, the MET detector offers unique advantages by eliminating the need for non-target reference data and exhibiting strong adaptive characteristics. Experimental results confirm its remarkable robustness in scenarios with other targets present in reference cells, achieving over 80% detection probability when the SCR is set to -5 dB with appropriate parameter adjustments. This work provides a comprehensive framework for enhancing radar target detection performance through advanced statistical methods and innovative detector designs.</p>

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Radar target detector based on banded sample autocovariance matrices

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月20日

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  • Chang Qu,
  • Xiaoying Wang,
  • Jing Chen,
  • Junping Yin,
  • Jiang Hu,
  • Zhigen Gao

摘要

Detecting weak radar targets in complex cluttered environments remains a significant challenge, particularly when attempting to effectively detect low signal-to-clutter ratio (SCR) targets while maintaining a constant false alarm rate (CFAR). We propose novel CFAR detectors based on time series analysis and statistical foundations. We model radar echo data within a coherent processing interval as stationary time series governed by linear random processes, enabling the application of a time series resampling approach to establish the autoregressive sieve bootstrap consistency of the banded sample autocovariance matrix (SACM) in the spectral norm. Leveraging this, we derive the numerical distribution of statistics related to the largest eigenvalue of the banded SACM. We introduce two improved CFAR detectors: one based on the banded SACM spectral norm (BSN detector) and another based on the likelihood ratio test in banded SACM eigenvalues (BLR detector). Additionally, we propose an adaptive CFAR detector, the maximum eigenvalue trimmed (MET) detector, developed using single-sample hypothesis testing. Our analysis demonstrates that detection probabilities stabilize as the number of bands exceeds a certain threshold, with robust performance under varying SCRs and false alarm probabilities. Simulations and real data experiments validate that all three detectors significantly outperform traditional radar target detection methods in terms of both detection performance and computational efficiency. Notably, the MET detector offers unique advantages by eliminating the need for non-target reference data and exhibiting strong adaptive characteristics. Experimental results confirm its remarkable robustness in scenarios with other targets present in reference cells, achieving over 80% detection probability when the SCR is set to -5 dB with appropriate parameter adjustments. This work provides a comprehensive framework for enhancing radar target detection performance through advanced statistical methods and innovative detector designs.