In high-resolution unmanned aerial vehicle (UAV) Synthetic Aperture Radar (SAR) imaging, despite the utilization of inertial navigation system (INS) data for pre-compensation, residual envelope and phase errors may still persist. Residual envelope errors can adversely affect phase error estimation (PEE), consequently degrading autofocus performance. This letter proposes a phase-envelope joint autofocus algorithm for backprojection (BP) imaging. An algorithm framework for alternating iterative estimation of phase and envelope is constructed based on maximizing image sharpness, which can effectively correct phase and envelope errors. In addition, only a small local area is selected for error estimation, significantly reducing memory and time consumption. The effectiveness of the algorithm is verified using X-band UAV SAR raw data.

An efficient raw data generation (RDG) algorithm in the hybrid domain is proposed, which can be applied to the accurate echo generation of spotlight mode synthetic aperture radar (SAR) with trajectory deviation, even in cases of terrain undulation. Generally, the accuracy required for the signal’s envelope is on the order of the range resolution cell. However, the requirement for the phase is significantly higher, on the order of the wavelength. Therefore, the proposed algorithm calculates the phase of the SAR raw data in the time domain through a point-by-point approach to ensure accuracy and computes the envelope of the raw data through subblock processing in the frequency domain to improve efficiency. To balance the computational efficiency and accuracy, the optimal selection of subblock size is discussed in detail. Simulation experiments verify the accuracy and efficiency of the method.

Due to the complexity of the range model and the severe range-azimuth coupling in the signal echoes during the diving flight of missile-borne synthetic aperture radar (SAR), the traditional frequency-domain algorithms have the limitation of accuracy in the processing of missile-borne SAR imaging, and the complexity of the algorithm is relatively high. To solve the problem of mismatch between the algorithm and the range model in the diving state, an improved parametric polar format algorithm (PPFA) based on equivalent range model is proposed. First, this letter transforms the diving trajectory model of the missile-borne into an equivalent range model applicable to horizontal straight flight. Then, based on the equivalent range model, and considering the spatial variability of the equivalent velocity and squint angle, we improve the azimuth-focusing operation of PPFA. These enhancements resolve the issue of poor imaging effect of edge points by using traditional PPFA, significantly improving the edge point focusing performance. The effectiveness and feasibility of the proposed algorithm are verified by the experimental simulation results and various indexes.

The existing Ground Cartesian Back-Projection (GCBP) algorithm is limited by its low effectiveness in spectral compression, which makes it unsuitable for processing high-squint and large-scale strip-map Synthetic Aperture Radar (SAR) data. To address this issue, we propose a novel algorithm called Ground Squint Fast Back Projection (GSFBP) for high-squint strip-map SAR imaging. First, we introduce a Ground Squint Coordinate (GSC) system that replaces the conventional Ground Cartesian Coordinate (GCC) system. The unique geometry of the GSC allows for precise rotation of the two-dimensional wavenumber spectrum without the need for auxiliary operations, significantly easing the constraints related to scene size.Moreover, the GSC framework facilitates seamless sub-image fusion through translation, eliminating the need for interpolation. Building on the original two-step spectral compression method, we have developed a GSC-specific relative spectrum inclination correction function to enhance spectral compression effectiveness. These innovations enable GSFBP to effectively manage large-scale scenes in high-squint SAR imaging. Experimental validation, using both simulated and real measured SAR data, confirms the superiority of the proposed algorithm.

Video image stability and imaging efficiency are two main problems in the application of current video synthetic aperture radar (ViSAR) systems. High resolution millimeter-wave ViSAR imaging is susceptible to inter-frame drift caused by motion errors, which seriously affects video stability. Image domain based registration algorithms would be affected by speckle noise, which causes distortions in synthetic aperture radar (SAR) images. Moreover, the high aperture overlapped rate in ViSAR results in redundant computations in multi-frame imaging. Therefore, how to achieve multi-frame fast imaging with high stability is a significant research direction of ViSAR. In this paper, a novel integrated method of fast multi-frame imaging and stabilization for video SAR is proposed. This approach mainly contains two aspects. First, in order to address video inter-frame drift compensation, the generating mechanism of drift error is analyzed from the imaging aspect. A gradient function is constructed to accurately estimate motion error by maximizing the correlation between sub-aperture images. Next, based on accurate frame motion error estimation, a sub-aperture complex-weighted fast back projection (SCFBP) algorithm is proposed, which combines the inter-frame motion error compensation in the signal domain. Simulations and real measured data experiments illustrate that the proposed method effectively improves the inter-frame stability and accelerates multi-frame imaging for video SAR.

In order to enhance radar target recognition performance of high resolution range profile (HRRP) under low signal-to-noise ratio (SNR), a novel HRRP sequential recognition method utilizing the correlation among scattering centers is proposed in this letter. In this method, the correlative information among the prominent scattering centers is considered in covariance matrix and the sequential recognition is carried out through the long short-term memory (LSTM) network to fully excavate the information of HRRPs. Moreover, we introduce a noise-robust recognition algorithm to renew the Gaussian trained model by the estimated variance of the noise. Experimental results indicate that the proposed method can acquire higher recognition rates and better robustness by introducing correlative information and sequential processing.

High-squint and wide-beam Synthetic Aperture Radar (SAR) imaging is challenging for current popular SAR imaging algorithms because the severe spatial-variant motion error precludes the precise Motion Compensation (MOCO). Especially the azimuth-variant motion error (AVME) would bring not only the azimuth-variant phase error but the non-negligible Nonsystemic Range Cell Migration (NsRCM). This paper proposes a novel SAR imaging algorithm to deal with the NsRCM and the azimuth-variant phase error in high-squint and wide-beam SAR imaging. For Range Cell Migration Correction (RCMC), a Parametric Keystone Transform Algorithm (PKTA) introducing the three-axis trajectory deviations as parameters is developed. It can correct the nominal RCM and the NsRCM through a time-variant scaling transform along slow time. The robust RCMC paves the way to precisely compensate for the azimuth-variant phase error. Following, a fast and precise subaperture MOCO algorithm, which applies the Recursive Discrete Fourier Transform (RDFT), is embedded in the azimuth focus procedure to adjust the azimuth-variant phase error. The proposed algorithm can handle the high-squint and wide-beam SAR data with severe motion errors based on these improvements. Finally, extensive experiments with simulated and real-measured SAR data demonstrate the proposal’s superiority and robustness.

Minimum entropy autofocus (MEA) has been applied in unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) imagery for its robustness in different circumstances. However, large amount of range cell samples to calculate the gradient for the minimum entropy optimization keeps its optimal convergence, which usually degrades the efficiency in real UAV SAR applications. In this letter, accelerated minimum entropy autofocus is proposed, which leverages both high computational efficiency and phase error estimation precision simultaneously. A strategy of stochastic gradient (SG) calculation is introduced in the MEA optimization with randomly selecting samples in each iteration through a probability distribution function (PDF). Experimental results with real UAV SAR data have validated the superior performance of the proposed SG-MEA algorithm.

This letter proposes a modified polar format algorithm (PFA) applied to the highly squinted synthetic aperture radar (SAR) onboard an accelerating hypersonic platform. In conventional PFA, the range wavenumber is orthogonally decomposed to obtain the 2-D wavenumbers corresponding to the imaging Cartesian coordinates. This operation, however, is inadequate when facing with more complex motion trajectory. In this letter, the essence of the proposed method is to homogenize the spatially nonuniformly sampled echoes by combining interpolation with the generalized wavenumber definition. The imaging polar coordinate system in the slant plane provides new space-wavenumber Fourier transform pair, and the 1-D interpolation compensates for the range-variant phase errors. The proposed algorithm, thus, enables fast and effective imaging of hypersonic SAR in large squint angles and high maneuverability dive mode. Its performance is discussed and analyzed in this letter, including the resolution and the size of the imaging scene. The effectiveness, superiority, and application value of the proposed algorithm are verified by simulation.

Highly squinted synthetic aperture radar (SAR) data focusing is a challenging problem with difficulty to correct the severe range-azimuth coupling and motion errors. Squint minimization processing with the range-walk correction is widely adapted to simplify the decoupling processing, while it destructs the azimuth-shift invariance of conventional SAR transfer function. In this paper, a two-stage focusing algorithm (TSFA) is proposed to generate a focused imagery for the highly squinted airborne SAR. In the proposed algorithm, conventional range cell migration correction and azimuth matched filtering are performed and a fine focusing stage is established to correct the azimuth variance. In the fine focusing procedure, the coarse-focused image is divided into azimuth blocks to accommodate the correction of azimuth-variant residual range migration and phase terms. Moreover, precise motion compensation is embedded into the TSFA procedure to form an accurate airborne SAR imagery, which may be called the extended TSFA. In order to balance the processing precision and computational load, optimal selection of block size is investigated in detail. Both simulated and real measured airborne SAR data sets are used to validate the proposed approaches.

Synthetic aperture radar (SAR) systems mounted on unmanned aerial vehicles (UAVs) are usually sensitive to trajectory deviations that cause serious motion error in the recorded data. In this letter, a novel range-dependent map-drift algorithm (RDMDA) is developed to accommodate the range-variant characteristics of severe motion errors. Utilizing the algorithm as a core estimate, we come up with a robust motion compensation strategy for the UAV SAR imagery. RDMDA outperforms the conventional MDA in both accuracy and robustness while it keeps similar efficiency. Real data experiment shows that the proposed approach is appropriate for precise imaging of UAV SAR systems equipped with only a low-accuracy inertial navigation system.