Airborne terahertz (THz) synthetic aperture radar (SAR) exhibits unique potential for ground moving target imaging (GMTIm), owing to its high frame rate and high-resolution capabilities. However, the short wavelength of THz waves significantly increases Doppler sensitivity. When a ground moving target performs curvilinear motion such as turns, velocity inconsistencies among scattering points induce variations in Doppler centroid frequencies and chirp rates, leading to defocusing and geometric deformation. To address these issues, an effective curvilinear moving target refocusing method is proposed in this letter. Firstly, a local phase gradient autofocus (LPGA) method is employed to compensate for Doppler chirp rate inconsistencies. Secondly, the additional spatial domain information from a dual-channel system is utilized to correct geometric deformation. Finally, both simulated and measured data are analyzed to validate the effectiveness of the proposed method.

Terahertz video synthetic aperture radar (THz-ViSAR) is emerging as a key technology for dynamic situational awareness and precise imaging of moving targets. However, the existing systems prioritize high frame rates at the expense of the dwell time, which degrades the azimuth resolution. To address this issue, we propose an improved video synthetic aperture radar (ViSAR) framework that can extend the synthetic aperture time and improve the image quality. First, the keystone transform (KT) is utilized to correct range cell migration, enabling accurate phase error estimation for high-resolution imaging. Moreover, unlike traditional methods without full-aperture imaging, our technique synthesizes a full-aperture phase error model by estimating subaperture phase errors, which can contribute to compensate for overall motion errors and generate high-resolution full-aperture SAR images. Airborne field experiments demonstrate the effectiveness of the proposed method, achieving the full-aperture high-resolution imaging while maintaining practical frame rates in dynamic surveillance scenarios.

In recent years, the application of passive millimeter-wave imaging (PMWI) in target detection, reconnaissance, and surveillance has drawn our attention again, especially passive interferometric millimeter-wave imaging (PIMI). However, PIMI suffers with high hardware and signal processing complexity, and passive interferometric millimeter-wave sensors (PIMSs) are very expensive. In this letter, a W/D-band millimeter-wave radiometer on unmanned aerial vehicles (UAVs) is first developed for target detection, reconnaissance, and surveillance, especially for ship detection. The W/D-band millimeter-wave radiometer has the advantages of low hardware and signal processing complexity and low cost. The W/D-band millimeter-wave radiometer is introduced, and a proof prototype of the W/D-band millimeter-wave radiometer is manufactured. Numerical simulations are performed to analyze the brightness temperature (TB) characteristic of the metallic ships. Outdoor experiments are performed to assess the feasibility of the metallic target detected by the W/D-band millimeter-wave radiometer on a UAV. Initial experimental results have demonstrated that metallic targets can be detected by the W/D-band millimeter-wave radiometer from UAVs, as expected.

Sequential ground moving target imaging (GMTIm) is an imperative and challenging task under terahertz video synthetic aperture radar (THz-ViSAR), which contributes to fine-grained situational awareness and moving target (MT) recognition. However, traditional GMTIm methods are usually designed for single-frame images, which involves repetitive parameter estimation for the sequential imaging problem and lacks the efficiency due to the parameter sensitivity. To tackle the aforementioned problems, this paper proposes a sequential GMTIm method based on hybrid THz-ViSAR-inverse SAR (ISAR) image formation. With respect to ViSAR processing, the sequential imaging results are firstly obtained. Considering the similarity of scene among inter-frame images, MTs can be detected based on target-level change detection after image registration and shadows left on the road. Following this, the defocused target region is transformed to obtain raw echoes, which is beneficial for parallel processing and reduce the computation amount. As for ISAR processing, the envelope alignment and auto-focus methods are employed to eliminate the residual motion errors and compensate for phase errors without constructing prior motion patterns. Thereafter, the ratio of equivalent rotational velocities between MTs and the scene is estimated to achieve the azimuth scaling. Finally, sparsity-based imaging enhancement is employed to further enhance the imaging quality. Simulations and airborne experiments are carried out to validate the effectiveness of the proposed method.

Ground moving target imaging (GMTIm) presents both challenges and opportunities for enhancing the fine-grained situational awareness and target recognition. However, the imaging quality of non-cooperative moving targets (MTs) fundamentally depends on their motion characteristics, which often fluctuate unpredictably during long dwell times in microwave bands. To address these limitations, this paper proposes a robust GMTIm scheme based on terahertz video synthetic aperture radar (THz-ViSAR). Rather than operating on complete echo data directly, the proposed method achieves GMTIm through the defocused region of interest (RoI) data inversion, which can reduce the computation burden. Benefiting from the short dwell time of THz-ViSAR, motion patterns of MTs can be simplified as the uniform motion in most cases. In response to range walk (RW) manifested by radial motion of MTs, the RW correction is employed based on the inclination of range profiles. Thus, considering the sparsity of MTs, motion errors caused by azimuth velocities from MTs, can be compensated by the minimum image entropy-based alternating direction method of multipliers (ADMM) autofocus framework, which can deal with the unknown phase errors and improve the imaging quality adaptively. Finally, the azimuth scaling is established by the azimuth spreading coefficient table, which is constructed based on the target velocity, rotating radius and center angle. Simulations and airborne field experiments are carried out to validate the effectiveness of the proposed method.

Airborne terahertz video synthetic aperture radar (THz-ViSAR) exhibits excellent superiorities in the dynamic situation awareness and moving targets detection. However, limited by the recording precision of the inertial navigation system (INS) or global positioning system (GPS), data-driven motion compensation (MOCO) methods are challenging to eliminate motion errors for THz band. Particularly, platform vibrations that are previously negligible in the microwave band, become non-negligible, drastically degrading the focusing performance. To conquer aforementioned problems, this paper proposes an echo-driven robust MOCO framework suited for THz-ViSAR. First, the airborne motion error model (MED) is re-derived and corresponding effects are analyzed in detail. Based on the derived MED, two parts are required to estimate from the echo: radial errors and vibration errors. Thus, a forward estimation of candidate signal of interests (SoIs) is proposed based on local peaks of the average range profile and the prominence. For each candidate, radial and vibration errors are estimated from the unwrapped phase of the SoI via the polynomial and multi-component sinusoidal fitting in turn. Finally, MOCO functions are constructed and evaluated based on the image entropy and image contrast, which determines the optimal SoI. Airborne field experiments are carried out to validate the effectiveness and practicability of the proposed method.

Rotating targets/components often carry beneficial information for identifying high-value targets in video synthetic aperture radar (ViSAR). However, unlike linear motion models, rotating motion models involve more parameters to be estimated, rendering parametric-based methods computationally intensive. Besides, such micro-motion features are relatively insignificant in the microwave band. To conquer the aforementioned problems, this article proposes the nonparametric indication and imaging of rotating targets under terahertz (THz) ViSAR. Taking rotating corner reflectors (RCRs) as the object, the Doppler prior and imaging characteristics (DPIC) are first determined and regarded as the theoretical basis. Combining DPIC and Doppler sensitivity of THz band, RCRs can be easily indicated by the nonparametric time–frequency transform. Thus, range cells of RCRs can be deduced intuitively by the visual saliency, followed by the separation of the residual background and defocused target area. After clutter suppression, two signal of interests (SoIs) of RCRs are extracted from salient points, where unwrapped phases and azimuth spectrums are utilized for fast estimation of phase errors and additional Doppler centroids (DCs), respectively. Finally, the refocused target image is stitched into the residual background. To validate the effectiveness of the proposed method, the airborne field experiment of RCRs is conducted in THz-band for the first time. Moreover, the anti-noise ability, relocation capability, and applicable speed interval of the proposed method are demonstrated, along with its extended application potential in terms of the ground moving target imaging.

The high-resolution inverse synthetic aperture radar (ISAR) imaging of space target holds tremendous significance for recognition and on-orbit maintenance. The terahertz (THz) radar possesses natural superiority in terms of large bandwidth and short wavelength, enabling it to achieve higher resolution both in range and azimuth directions. However, the range cell migration (RCM) induced by rotation is severe in the THz band, even in small rotation angle. In order to achieve high-accuracy RCM correction, an imaging algorithm based on keystone transform (KT) and fast Gaussian gridding nonuniform fast Fourier transform (FGG NUFFT) is presented in this article. Nevertheless, to correct the second-order RCM and compensate the spatial-variant (SV) phase error, the rotation parameters of uncooperative target need to be estimated first. Moreover, by analyzing the ISAR images of space targets, it is found that the scattering intensity of certain components on the satellite body, such as the cavity structure of the antenna, is stronger than that of the solar panels. Considering scattering intensity of these components, the traditional estimation method under global image quality criteria may fail. To prevent getting trapped in a local optimum or finding an incorrect solution due to the influence of strong scattering points, a parameter estimation framework based on prior knowledge added sparrow search algorithm (PKSSA) under local maximum contrast criterion (LMCC) is brought forward. The proposed novel algorithm can not only fast estimate rotation parameters without being affected by strong scattering points but also obtain fine imaging results. Numerous electromagnetic simulation and real measured THz experiment results prove the correctness and effectiveness of the proposed algorithm.

As demand for precise detection of space targets and orbital maintenance increases, it is crucial to obtain high-resolution images of space targets. The terahertz (THz) radar with carrier frequency of 0.1-10 THz has the characteristics of larger bandwidth and shorter wavelength compared with microwave radar. Accordingly, under the same inverse synthetic aperture radar (ISAR) imaging conditions, it has advantages in both range resolution and azimuth resolution theoretically. However, for large-size space targets, the phenomenon of high-order range cell migration (RCM) and azimuth phase error are prone to occur during small-rotation-angle (3-5°) ISAR imaging, which results in severe imagery defocusing. In this article, a high-resolution ISAR imaging method based on defocused point spread function (PSF) is proposed. By means of third-order Taylor expansion performing on the Range-Doppler (RD) imaging model, the impact of high-order terms on imaging is analyzed. Through revealing the relationship between high-order terms and inclination of defocused PSF measured with Hough Transform (HT), the rotation velocity and range center deviation (RCD) are estimated accurately and independently in image domain. After parameters estimation, the first-order RCM is corrected by Keystone Transform (KT). Utilizing the estimated rotation velocity, the second-order RCM is eliminated by Fast Gaussian Gridding Nonuniform Fast Fourier Transform (FGG NUFFT). Then the spatial-variant phase error is compensated based on previous estimation. Lastly, azimuth resolution is improved by FGG NUFFT due to non-uniformly sampled data. Numerous simulations and real measured experimental results demonstrate the effectiveness and outperformance of proposed algorithm for large-size space target ISAR imaging in the THz band.

Video synthetic aperture radar (ViSAR) operating at low-frequencies generally suffers from low frame-rate and high latency due to its wide synthetic aperture and long dwell time. To conquer the problems mentioned above, this article proposes a ViSAR imaging processing framework based on the terahertz (THz) regime. The proposed method only needs a data repetition ratio of 0% rather than 80% for the microwave to reach the 0.2m imaging resolution and 5Hz frame rate with low latency thanks to the designed high pulse repetition frequency (PRF) system and short wavelength of THz wave. Thus, robust estimations of baseband Doppler centroid and Doppler rate are proposed based on the multi-core adaptive weighting and imaging knowledge prior, which handles the Doppler sensitivity of the THz regime effectively. Finally, airborne experiments are carried out to validate the superiority of dynamic situational awareness and the potential of moving targets indication in the THz-ViSAR.

Airborne distributed coherent aperture radar (ADCAR) coheres several flexible unit radars to form an equivalent large aperture radar for enhancing the signal gain, spatial resolution and system reliability. However, the ultra-long interelement spacing brings severe grating lobes, which will considerably deteriorate the performance of clutter suppression and target detection. To conquer this issue, we propose a stepped frequency signal based space-time adaptive processing (STAP) approach for clutter and grating lobes suppression. The increased transmitting signal frequency can expand the space frequency and Doppler frequency of both target echo and clutter echo. Therefore, the target and clutter can be distinguished through different expanded frequency ranges, and in turn the grating lobes can be suppressed. To further reduce the peak sidelobe ratio of target response, accumulated pattern synthesis approach is applied in both transmit and receive end. Furthermore, to guide practical implementation, the influence of the non-ideal factors including gain errors, phase errors, platform control errors and grid mismatch are analyzed to provide a feasible boundary for the algorithm. Simulations corroborate the superiorities of the proposed STAP approach for ADCAR.

Terahertz (THz) regime has shown superior performance in terms of reflecting the details of target structures. However, the extended structures (ESs) may be discretized into endpoints in synthetic aperture radar (SAR) images if the observation aperture deviates from the specular orientation of ESs, which deteriorates subsequent image interpretation and intelligent imaging. Taking the tank barrels as the object, this paper proposes a novel solution to detect and reconstruct the critical ES by effectively exploiting the component prior and imaging characteristics (CPIC). The core of the proposed method lies in converting the phase matching of the multi-views methods into object detection based on CPIC and deep neural network. Firstly, the CPIC of tank barrels is analytically determined and regarded as the theoretical basis of datasets annotation. Thus, the modified multi-scale object detection network is constructed to enhance the detection performance and estimate the orientation of barrels. Finally, comprehensive simulation, anechoic chamber and field experiments are carried out to validate the effectiveness of the proposed methods. The results show that the proposed method can outperform the existing object detection networks in terms of the detection accuracy of multi-scale objects, and outperform the existing multi-view methods in terms of time need with comparable accuracy.