Compared with narrowband radars, the high-resolution range profile (HRRP) acquired by wideband radars contain abundant target information that is useful for detection and recognition. However, more and more threatening targets are of low observability and high maneuverability. Therefore, reconstructing HRRPs of maneuvering weak targets from low signal-to-noise ratio (SNR) echoes is a significant but challenging problem. To estimate motion parameters and reconstruct high-SNR HRRP, this paper proposes a long-time coherent integration (LTCI) algorithm based on subband keystone transform (SKT) and extended Lv's distribution (ELVD). In this paper, the range-spread target (RST) assumption is applied in the LTCI processing. Moreover, the proposed SKT-ELVD operations have excellent performance on range migration (RM) correction and integration gain. The proposed algorithm can be of effectiveness in lower SNR conditions, since more energy of target's echo can be accumulated. Theoretical results are initially supported by sufficient simulation examples. Finally, the validity of the proposed algorithm is confirmed by experimental data.

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.

This paper intends to address the long-time coherent integration (LTCI) of high maneuvering targets in low signal-to-noise (SNR) environments with high computational efficiency and accuracy. The high-order motion parameters estimation, considering the acceleration and jerk, is modeled as the under-estimated linear regression and the complex-field Bayesian compressive sensing (BCS) algorithm is introduced to resolve the sparse recovery. To correct the high-order range migration (RM) and reduce the order of Doppler frequency migration (DFM), the adjacent cross correlation (ACCF) is applied. An effective method based on non-coherent integration is proposed to extract the self-term from ACCF result. Further, with the proper design of sensing matrix, the acceleration and jerk motion parameters are estimated by sparse reconstruction based on the complex-field BCS. Compared with the traditional methods based on time-frequency transform, like Lv's distribution (LVD), the proposed BCS algorithm is free from the interference of cross-terms and maintains the super-resolution ability, which provides better performance in parameters estimation and multi-targets discrimination. Finally, the numerical experiments validate the advantages of the proposed method in motion parameters estimation, super-resolution ability and computational efficiency.

Effective suppression of mainlobe deceptive jamming targets ensure that radar systems accurately and reliably detect and track true targets in complex electromagnetic environments. For the self-screening jamming scenario, this study proposes a method based on the frequency-diverse array (FDA)-multiple-input multiple-output (MIMO) radar to suppress mainlobe deceptive jamming targets effectively, which consists of a series of steps. In warning stage, a random logarithmic frequency offset is designed to obtain the unambiguous range and angle parameters of targets, and it is proved that under this offset, the range parameter in receiver beampattern of FDA-MIMO radar is unambiguous. In jamming rejection stage, a $M\times 1$ dimensional unambiguous range compensation vector is constructed based on the random logarithmic frequency offset to suppress the jamming targets generated by self-protection jammers. By compensating the transmission steering vector coefficients of targets, the true, mainlobe deceptive, and sidelobe angle-deceptive jamming targets can be distinguished on the combined transmission-reception spatial frequency domain. Finally, to separate the true target signal from the jamming signal at the receiver end, an adaptive filter is designed. Due to the proposed compensation process, the adaptive filter only needs to match the true target and suppress angle-deceptive jamming targets, without the need to suppress mainlobe deceptive jamming targets by setting null steering regions. Additionally, a singular spectrum analysis algorithm is used to select samples to estimate the exact covariance matrix of the interference plus noise. Numerical simulations verify that the proposed method has better robustness and effectiveness than the existing methods in suppressing mainlobe deceptive jamming targets.

The spaceborne synthetic aperture radar (SAR) system working at P-band, is vulnerable to the ionospheric effect. The ionospheric scintillation will introduce random phase fluctuations into the SAR signal and deteriorate the imaging performance. In this paper, a minimum-entropy autofocusing method based on the intelligent optimization strategy is proposed to compensate the scintillation phase error in spaceborne P-band SAR images. A refined particle swarm optimization (Re-PSO) is proposed to provide an intelligent strategy in SAR autofocusing. Compared with the traditional minimum-entropy autofocusing methods, the proposed Re-PSO algorithm is a heuristic method which has extremely strong exploring abilities to the global optimum. The genetic multi-crossover operator and the gradient accelerator are utilized to improve the convergence property of the basic PSO. Furthermore, since the isolate strong scatterers are not required in minimum-entropy SAR autofocusing, the proposed method has strong robustness. The simulations on point and area targets validate the effectiveness and better performance of the proposed method.

This paper intends to address the long-time coherent integration (LTCI) of high maneuvering unmanned aerial vehicle (UAV) in low SNR environment with the idea of sparse reconstruction. Due to the small radar cross section (RCS), high maneuverability and micro-Doppler (m-D) effect, the robust detection of weak UAV target is a challenging problem. Both of the across range unit (ARU) effect and Doppler frequency migration (DFM) will be introduced in UAV returns due to high maneuverability. Furthermore, m-D effect induced by the rotation of rotor blades will also disturb the LTCI performance. To address these problems, a LTCI method called KT-BCS-LSM is proposed for high maneuvering UAV detection. To mitigate the disturbance of m-D effect, the null space pursuit (NSP) with third-order differential operator is constructed to separate the m-D signal from UAV returns. Then, the keystone transform (KT) is applied for range walk correction. Motion parameters estimation, including velocity, acceleration and jerk, is modeled as a sparse representation problem and solved by Bayesian compressive sensing (BCS) with Laplacian scale mixture (LSM) prior. Compared with the searching-based method, like generalized RFT (GRFT), the proposed method has significant higher computational efficiency. Compared with transform-based methods, like fractional Fourier transform (FrFT) and Lv's distribution (LVD), the proposed algorithm is able to compensate the DFM induced by jerk motion and has higher estimation accuracy and noise robustness. The effectiveness of proposed method is validated by numerical simulations and real-measured data of a DJI UAV.