In passive bistatic radar,the computational efficiency of clutter suppression algorithms remains low,due to continuous increases in bandwidth for potential illuminators of opportunity and the use of multi-source detec...In passive bistatic radar,the computational efficiency of clutter suppression algorithms remains low,due to continuous increases in bandwidth for potential illuminators of opportunity and the use of multi-source detection frameworks.Accordingly,we propose a lightweight version of the extensive cancellation algorithm(ECA),which achieves clutter suppression performance comparable to that of ECA while reducing the computational and space complexities by at least one order of magnitude.This is achieved through innovative adjustments to the reference signal subspace matrix within the ECA framework,resulting in a redefined approach to the computation of the autocorrelation matrix and cross-correlation vector.This novel modification significantly simplifies the computational aspects.Furthermore,we introduce a dimension-expanding technique that streamlines clutter estimation.Overall,the proposed method replaces the computation-intensive aspects of the original ECA with fast Fourier transform(FFT)and inverse FFT operations,and eliminates the construction of the memory-intensive signal subspace.Comparing the proposed method with ECA and its batched version(ECA-B),the central advantages are more streamlined implementation and minimal storage requirements,all without compromising performance.The efficacy of this approach is demonstrated through both simulations and field experimental results.展开更多
基金supported by the Zhejiang Provincial Natural Science Foundation of China(No.LZ23F030002)the Science and Technology Program of Zhejiang Provincial Department of Transportation(No.2024012)the Talent Funding Project of Zhejiang Institute of Communications(Nos.822321KY0127 and 2024JK05)。
文摘In passive bistatic radar,the computational efficiency of clutter suppression algorithms remains low,due to continuous increases in bandwidth for potential illuminators of opportunity and the use of multi-source detection frameworks.Accordingly,we propose a lightweight version of the extensive cancellation algorithm(ECA),which achieves clutter suppression performance comparable to that of ECA while reducing the computational and space complexities by at least one order of magnitude.This is achieved through innovative adjustments to the reference signal subspace matrix within the ECA framework,resulting in a redefined approach to the computation of the autocorrelation matrix and cross-correlation vector.This novel modification significantly simplifies the computational aspects.Furthermore,we introduce a dimension-expanding technique that streamlines clutter estimation.Overall,the proposed method replaces the computation-intensive aspects of the original ECA with fast Fourier transform(FFT)and inverse FFT operations,and eliminates the construction of the memory-intensive signal subspace.Comparing the proposed method with ECA and its batched version(ECA-B),the central advantages are more streamlined implementation and minimal storage requirements,all without compromising performance.The efficacy of this approach is demonstrated through both simulations and field experimental results.
