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In the scene of ground moving target detection, the target echo information is often corrupted by strong clutter. In this letter, a joint clutter suppression algorithm is proposed based on robust principal component analysis and total variation denoising for pulse Doppler radar. The novel method named total variation‐ robust principal component analysis replaces the l1‐norm in robust principal component analysis algorithm with a total variation seminorm. Total variation robust principal component analysis retains the clutter suppression ability of robust principal component analysis, and effectively separates the target from the noise in range‐Doppler domain. [ABSTRACT FROM AUTHOR]

Range ambiguity suppression is a significant problem in traditional pulse‐Doppler radar systems, especially when the pulse repetition frequency is set high to avoid Doppler ambiguity. One possible way to deal with such problem is to apply the noise radar system, by transmitting pulse‐agile waveforms and using a group of matched filters as the receiver to accumulate the power of unambiguous echoes while dispersing the range‐ambiguous ones. However, when strong targets or heavy clutters exist, the dispersed ambiguous power is still high, which leads to high range‐Doppler sidelobes and overwhelms the weak targets. This paper explores a new way to deal with the range‐ambiguity suppression problem in noise radar. The range‐ambiguity suppression as a sparse recovery problem is formulated, and then both the unambiguous and ambiguous targets (and/or clutters) can be simultaneously reconstructed. The proposed method can enhance the range‐ambiguity suppression performance and synthetic an ambiguity‐free range‐Doppler map with a low sidelobe level, which ensures the weak target recovery performance even in a cluttered environment. [ABSTRACT FROM AUTHOR]