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In recent years, super-resolution imaging techniques have been intensely introduced to enhance the azimuth resolution of real aperture scanning radar (RASR). However, there is a paucity of research on the subject of sea surface imaging with small incident angles for complex scenarios. This research endeavors to explore super-resolution imaging for sea surface monitoring, with a specific emphasis on grounded or shipborne platforms. To tackle the inescapable interference of sea clutter, it was segregated from the imaging objects and was modeled alongside I/Q channel noise within the maximum likelihood framework, thus mitigating clutter's impact. Simultaneously, for characterizing the non-stationary regions of the monitoring scene, we harnessed the Markov random field (MRF) model for its two-dimensional (2D) spatial representational capacity, augmented by a quadratic term to bolster outlier resilience. Subsequently, the maximum a posteriori (MAP) criterion was employed to unite the ML function with the statistical model regarding imaging scene. This hybrid model forms the core of our super-resolution methodology. Finally, a fast iterative threshold shrinkage method was applied to solve this objective function, yielding stable estimates of the monitored scene. Through the validation of simulation and real data experiments, the superiority of the proposed approach in recovering the monitoring scenes and clutter suppression has been verified. [ABSTRACT FROM AUTHOR]

This letter deals with the range-Doppler image reconstruction problem in co-located multiple input multiple output (MIMO) noise radar, where the waveform diversity is applied not only among different transmit channels but also among different pulse repetition intervals (PRI). The transmit channel diversity and pulse diversity lead to high range-Doppler sidelobes, which would cause the weak target to be overwhelmed by strong target or clutters. In this letter, we propose a sparse recovery-based approach to deal with the range-Doppler image reconstruction problem in collocated MIMO noise radar, by exploiting the 2D-block sparsity of range-Doppler profiles to enhance the range-Doppler image reconstruction performance and its robustness to clutter. Simulation results show that the range-Doppler image reconstruction performance of the proposed method outperforms traditional methods, especially when noise and strong clutter exist. [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]

High sidelobes exist in normal multiple-input multiple-output (MIMO) radar waveforms, which utilize linear frequency modulation (LFM) or non-linear frequency modulation (NLFM) waveforms with the same subcarrier durations. Since the waveforms have the same expression, and each segment of the subcarriers can be only controlled by a few parameters to make them distinctive. This will cause comparatively high auto-correlation sidelobes in each zone of subcarriers. In order to suppress the sidelobes, new piecewise NLFM waveforms have been proposed in this paper. These novel MIMO radar waveforms are composed of three different kinds of subcarriers with totally different mathematical expressions. This is verified by the fact that the cross-correlation sidelobes between different subcarriers are much lower than the auto-correlation of each subcarrier. In addition, the number of MIMO radar waveforms has been improved significantly. As different subcarriers have their own controllable parameters, a genetic algorithm will be applied to generate the novel MIMO radar waveforms. Numerical results demonstrate the effectiveness that the auto-correlation sidelobes and the cross-correlation sidelobes of our proposed waveforms are reduced. [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]

Abstract: A high-resolution estimation of velocity and range profile (HREVRP) method is studied to yield both high-resolution radial velocity and range profile of slowly moving target for pulse linear frequency modulation (LFM) ultra-wideband (UWB) radar. Two-dimensional projection discrete Fourier transform (2-DPDFT) is utilized to estimate the velocity. The range profile is then obtained by compensating its motion and spread with this velocity estimate. In addition, a range of the velocities is derived based on velocity ambiguity and corresponding phase restriction. The proposed method does not require any initial velocity estimation and focusing matrices construction. Besides, it has superior performance of velocity estimation compared with conventional techniques, especially at low signal-to-noise ratio (SNR) levels. The simulations validate the effectiveness of the method. [Copyright &y& Elsevier]

• For the maneuvering target detection, the proposed method can remove range migration (RM) and Doppler frequency migration (DFM) in both fixed and random pulse repetition interval (PRI) radar systems. • The proposed method has a high search efficiency thanks to the adoption of sparse Fourier transform (SFT). • As a linear algorithm, the proposed method has a good performance in the scenario of multiple weak targets detection. The range migration (RM) and Doppler frequency migration (DFM) are the two major problems faced by radar maneuvering target detection. Unlike the existing algorithms which focus on the correction of RM and DFM in the fixed pulse repetition interval (PRI) system, this paper proposes a novel method which can further realize coherent integration in the random PRI system. The proposed method firstly conducts the multi-dimensional search for the maneuvering target's trajectory, then compensates DFM and realizes coherent integration via the nonuniform polynomial sparse Fourier transform (NUPSFT). As a linear algorithm, the proposed method can detect multiple weak targets without generating any cross term. Moreover, a high processing efficiency can be obtained thanks to the application of sparse Fourier transform (SFT). Both theoretical analysis and simulation results have validated the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]