Search

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]

This paper presents an algorithm to enhance the range‐Doppler imaging performance for noise radar. Traditional matched filtering based method suffers from high range and Doppler sidelobes, which makes the weak targets overwhelmed by the sidelobes of strong targets or clutters in the range‐Doppler map. Sparse recovery based methods have been widely used to suppress such sidelobes, but most of them assume a repetitive transmit waveform, which cannot be applied to noise radar applications. In this letter, the range‐Doppler imaging problem for noise radar is formulated as a sparse recovery model and solved by a 2D generalized smoothed‐l0 algorithm. The proposed method can deal with the random waveforms varying among different pulses in noise radar. The robustness of the proposed method in strong noise and clutters is validated by simulation results. [ABSTRACT FROM AUTHOR]

An iterative adaptive approach (IAA) based algorithm is presented to realise coherent integration for the random pulse repetition interval radar. The proposed method firstly removes the effect of Doppler ambiguity via phase compensation, and then accomplishes target focusing via conducting the azimuth IAA and range inverse fast Fourier transform. Theoretical analysis and numerical experiments have verified the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]