In this letter, the low peak sidelobe level (PSL) mismatched filter (MSF) designed for coherent frequency diverse array (FDA) radar using phase-coded waveform is proposed. Considering the space-time coupled characteristic of coherent FDA, the proposed MSF incorporates the equivalent transmit beamforming and low sidelobe range compression simultaneously. The minimization of PSL in MSF design is modeled as a second-order cone programming (SOCP) problem and solved by convex optimization. Furthermore, two modified algorithms are proposed to improve the spatial clutter suppression and Doppler tolerance in original framework. The proposed methods will help to expand the applications of coherent FDA radar in wide-covered and high-resolution observation.

The adjacent cross correlation function (ACCF) is an effective tool to remove the range migration (RM) and reduce the order of Doppler frequency migration (DFM) in the pulsed-radar echoes of maneuvering targets, and thus has been employed in radar target detection and imaging. However, the ACCF is a nonlinear transform, which means that the signal-to-noise ratio (SNR) variation will be introduced by this operation. This letter presents a quantitative analysis of SNR for the ACCF. The frequency-domain derivation and analyses of ACCF are presented. Based on this, the SNR variation of the ACCF is analytically derived. The theoretical derivations and analyses are confirmed by numerical examples.

Abstract Bayesian compressive sensing (BCS) is an important sub‐class of sparse signal reconstruction algorithms. In this paper, a modified complex multitask Bayesian compressive sensing (MCMBCS) algorithm using the Laplacian scale mixture (LSM) prior is proposed. The LSM prior is first introduced into the complex BCS framework by exploiting its better sparse characteristic and flexibility than traditional Laplacian prior. Furthermore, by integrating out the noise variance analytically, the MCMBCS algorithm significantly improves the signal recovery performance than the original CMBCS. More importantly, the authors not only present the iterative algorithm but also develop the sub‐optimal fast implementation method based on the marginal likelihood maximisation, which dramatically reduce the computational complexity. Finally, sufficient numerical simulations validate the better performance of the proposed algorithm in reconstruction accuracy and computational effectiveness than existing work. It is revealed that the proposed algorithm has great potential in the complex‐valued signal processing field.

The spaceborne low-frequency (L-band and below) synthetic aperture radar (SAR) is very susceptible to ionospheric scintillation. The scintillation phase error (SPE) is a vital factor that brings about the azimuth decorrelation and leads to the imaging degradation. In this article, a methodology is described, which exploits the accurate SPE estimation to measure scintillation parameters from SAR images. First, the phase gradient autofocus (PGA) is applied to achieve the believable SPE estimate from the local area that contains a strong scatterer. Second, based on the estimated staggered index of two SPE estimates, the irregularity altitude can be derived by solving an established equation using an iteration process. Third, three parameters, including the spectrum index, the outer scale, and the integrated turbulence strength, can be fitted when the theoretical spectrum expression mostly approximates the derived spectrum. The methodology is validated on an ALOS-2 PALSAR-2 spotlight data, and its azimuth imaging was seriously degraded by ionospheric scintillation. Two existent corner reflectors ensure the accuracy of SPE estimates from PGA. The measurement results indicate that the local ionospheric irregularities are extremely turbulent and located at a lower altitude of about 220 km in the F2-layer.