Traditional SAR Research Articles

Leveraging Visual Language Model and Generative Diffusion Model for Zero-Shot SAR Target Recognition

Simulated data play an important role in SAR target recognition, particularly under zero-shot learning (ZSL) conditions caused by the lack of training samples. The traditional SAR simulation method is based on manually constructing target 3D models for electromagnetic simulation, which is costly and limited by the target’s prior knowledge base. Also, the unavoidable discrepancy between simulated SAR and measured SAR makes the traditional simulation method more limited for target recognition. This paper proposes an innovative SAR simulation method based on a visual language model and generative diffusion model by extracting target semantic information from optical remote sensing images and transforming it into a 3D model for SAR simulation to address the challenge of SAR target recognition under ZSL conditions. Additionally, to reduce the domain shift between the simulated domain and the measured domain, we propose a domain adaptation method based on dynamic weight domain loss and classification loss. The effectiveness of semantic information-based 3D models has been validated on the MSTAR dataset and the feasibility of the proposed framework has been validated on the self-built civilian vehicle dataset. The experimental results demonstrate that the first proposed SAR simulation method based on a visual language model and generative diffusion model can effectively improve target recognition performance under ZSL conditions.

A 0.49–4.34 μW LC-SAR Hybrid ADC with a 10.85-Bit ENOB and 20 KS/s Bandwidth

This paper presents a level-crossing successive-approximation-register (LC-SAR) hybrid analog-to-digital converter (ADC) that combines an LC ADC with an SAR ADC, which may be used for Internet of Things (IoT) random sparse event scenarios. The sampling frequency of a traditional LC ADC is usually proportional to the maximum instantaneous rate of change of the input signal; therefore, a higher input signal frequency inevitably leads to higher system power consumption. However, the proposed hybrid ADC uses the input level difference between the two moments before and after level-crossing detection, thereby ensuring a higher conversion precision and lower power consumption, even at higher input signal frequencies. Compared with traditional LC ADC or SAR ADC, the proposed hybrid ADC combines the ultralow-power advantage of LC ADC with the high-precision advantage of SAR ADC in converting IoT data with sparse characteristics such as ECG, EEG, and brain potential. The LC-SAR hybrid ADC is designed with a 0.18 μm CMOS process and consumes 4.34 μW at a 1.8 V supply voltage, achieving an SNDR of 67.41 dB and a bandwidth of 20 kHz. The spectrum analysis result was 10.85 ENOB when the input sinusoidal signal was 14.975 kHz. When inputted with an ECG signal, the system power consumption was as low as 0.49 μW. Furthermore, the proposed hybrid ADC obtained a good figure of merit, with FoMw and FoMs reaching 58.8 fJ/conv.steps and 164 dB, respectively. Compared to a conventional uniform sampling ADC, approximately 80% of the power savings and an 8x compression ratio can be achieved in physiological signal acquisition applications.

A Modified Iteration-Free SPGA Based on Removing the Linear Phase

In traditional Stripmap SAR imaging, the platform motion error will bring the phase error in the azimuthal direction to the image, which will have a series of effects on the imaging quality. The traditional autofocus algorithm—Stripmap Phase Gradient Algorithm (SPGA)—can estimate any order phase error above the second order in theory, but it is difficult to estimate the linear phase error, which leads to the discontinuity of the estimated phase error. It usually needs multiple iterations to focus an image, which is inefficient. Moreover, because the linear phase error cannot be estimated, the traditional SPGA cannot eliminate the target offset in the image, resulting in the distortion of the image in the azimuthal direction. According to the continuity of phase error, we propose a modified iteration-free SPGA based on removing the linear phase. Without iteration, the proposed autofocus algorithm can achieve comparable or even better results than traditional SPGA. In the simulation experiments, piecewise linear errors are added to the images of multiple targets. SPGA still fails to focus the image after six iterations. The average ILSR and ILSR are −7.11 dB and −3.99 dB, respectively, and the average number of point target drift is 8.42 pixels. The proposed algorithm optimizes the average ILSR and ILSR to −12.34 dB and −9.87 dB and reduces the average number of point target drift to 0.16 pixels. In the actual data processing, using image entropy as the evaluation criterion, the time consumption is only 19.25% of SPGA under the condition of achieving the same focusing quality.

A Fast Matching Method for the SAR Images with Large Viewing Angles Based on Inertial Navigation Information and Neighborhood Structure Consensus

In the field of multi-view SAR target location, the greater the difference in viewing angles, the higher the target location accuracy. However, this makes it difficult to match the same target between the SAR images with different viewing angles. The performance of traditional SAR image-matching algorithms will deteriorate or even fail to match the images correctly when the viewing angle is gradually increased. To solve this problem, a fast SAR matching method for the SAR images with large viewing angles based on inertial navigation information and neighborhood structure consensus (ININSC) is proposed in this paper. In this algorithm, the key targets are detected in the SAR images by using the maximum connected domain algorithm and the K-means clustering algorithm, and the connected domain centroid of the target is taken as a feature point. The approximate position of the key targets after the viewing angle change is found through inertial navigation information, and then accurate and fast matching is achieved by using the consensus of the neighborhood topological structure of the key targets. The measured data sets demonstrate that compared with traditional SAR image-matching algorithms, the proposed ININSC algorithm solves such a problem that SAR images cannot be accurately matched under the differences at large viewing angles and has better robustness and timeliness.

Design of a 12 bit SAR ADC with Self-Calibration

Successive approximation analog-to-digital converter (SAR-ADC) are widely used in intelligent sensing fields due to it’s low power consumption, medium and high precision and such characteristics. Traditional binary SAR ADC have non-ideal factors such as DAC capacitor array mismatch and comparator offset, which severely limit it’s performance. Therefore, a self-calibration method for non-ideal factors is proposed in this work, and theoretical derivation and simulation analysis are carried out. A 12-bit non-binary SAR ADC with self-calibration is designed based on the TSMC 40nm CMOS process, which reduces the capacitance area, improves the conversion accuracy, and eliminates most of the errors caused by non-ideal factors. Simulation results show that the ADC’s SNDR is 72.01dB, and INL within +1.2/-1LSB, meets most of the market requirements.

Analysis and Compensation of Sigma-Delta ADC Latency for High Performance Motor Control and Diagnosis

High performance motor control and fault diagnosis requires high resolution isolated signal measurement. Sigma-delta ADCs (SD-ADC) are ideal for this purpose but cause remarkable measurement latency which can cause performance degradation and stability issues. In this paper, the effects of current measurement latency caused by a sigma-delta filter module (SDFM) and corresponding latency compensation are discussed in detail. First, the effect of SD-ADC on system phase margin and ADC update delay is analyzed in detail. Consequently, a Kalman filter based technique is proposed to compensate the latency caused by the group delay of the digital filter and increase the phase/gain margin of the system. In addition, three PWM update strategies are proposed and their tradeoffs discussed. The proposed algorithm and measurement results are experimentally verified on a PMSM drive. It is shown that with the proposed solution, Sigma-delta ADC can provide much higher resolution than traditional SAR ADCs and also yield superior diagnostics capability and control.

Elevation Multi-Channel Imbalance Calibration Method of Digital Beamforming Synthetic Aperture Radar

The digital beamforming synthetic aperture radar (DBF-SAR) is proposed by scholars as a promising solution to overcome the constraint of the minimum antenna area of the traditional single-channel SAR to achieve high resolution and wide swath (HRWS) by scan-on-receive (SCORE) in the elevation multiple channel. However, the inevitable channel imbalance between the elevation channels of DBF-SAR will degrade the DBF-SAR image quality. In this paper, we present a method to estimate the sampling time delay error, amplitude error and phase error based on the external calibration data. For the sampling time delay deviation, we adopt to calculate the statistical average of the position deviation of several external calibration points in the reference channel image with that of the error channel image. To avoid noise interference, we image the DBF-SAR original echo-carrying amplitude information to obtain the amplitude error between channels by dividing the absolute values of the complex image data of the error channel. Due to the phase error between channels, the image contrast will decrease. Therefore, the problem of estimating the phase error can be transformed into the problem of maximizing the image contrast. So, in this paper, we use the gradient descent method to optimally estimate the phase error. Finally, the effectiveness of the method is verified by the simulation of airborne measured data and simulation data.

A novel solution for stereo three-dimensional localization combined with geometric semantic constraints based on spaceborne SAR data

Improved resolution and geolocation accuracy of spaceborne SAR images make refined digital surface model extraction and delicate three-dimensional reconstruction promising. Currently, extracting a refined digital surface model by stereo SAR remains challenging due to the significant matching errors. In this paper, we quantitatively analyze the effect of matching errors on stereo SAR based on a simulation method. Subsequently, a novel stereo SAR approach combined with geometric semantic constraints is proposed to reduce the effect of matching errors on localization. In this approach, a stereo localization model combined with geometric semantic constraints is utilized to jointly locate a group of points with planar or linear structures. The least squares solution of the system of equations will be solved by an iterative method based on correcting characteristic values. Finally, the simulation experiments show that the planar and linear semantic constraints improve the relative positioning accuracy by about 26% and 70%, respectively. Experiments based on real data also demonstrate that the proposed method is significantly better than traditional stereo SAR.

An Adjusted Frequency-Domain Algorithm for Arc Array Bistatic SAR Data with One-Moving Transmitter.

Arc array synthetic aperture radar (AA-SAR), which can observe the scene in all directions, breaks through the single view of traditional SAR. However, the concealment of AA-SAR is poor. To mitigate this, arc array bistatic SAR (AA-BiSAR) with the moving transmitter is proposed, it has the advantages of good concealment and can expand the imaging scene, and improve the flexibility of the system. The imaging geometry including the signal model is established, and a range frequency-domain algorithm based on keystone transform (KT) is proposed in this paper. In the first step, the slant range equation is approximated by Taylor series expansion to compensate for the residual phase caused by the transmitter motion. In the second step, the range cell migration between the range and azimuth is eliminated through the KT method in the range frequency-domain. In the third step, using the data after range cell migration correction in step 2, an azimuth pulse compression is performed to obtain the focused image. In addition, the spatial resolution of the AA-BiSAR system is analyzed in detail. Finally, three simulation results verify the effectiveness of the proposed algorithm and the change in the spatial resolution.

A Long-Time Coherent Integration STAP for GEO Spaceborne-Airborne Bistatic SAR

A geosynchronous spaceborne-airborne bistatic synthetic aperture radar (GEO SA-BSAR) system is an important technique to achieve long-time moving target monitoring over a wide area. However, due to special bistatic configuration of GEO SA-BSAR, two major challenges, i.e., severe range migration and space-variant Doppler parameters for moving targets, hinder the moving target indication (MTI) processing. Traditional SAR MTI methods, which do not take the challenges into consideration, will defocus the moving targets, leading to a loss of the signal-to-noise ratio (SNR). To focus moving targets and estimate motion parameters accurately, long-time coherent integration space-time adaptive processing (LTCI-STAP) is proposed for GEO SA-BSAR MTI in this paper. First, a modified adaptive spatial filtering based on the bistatic signal model is performed to suppress the clutter. Then, an LTCI filter bank is constructed to achieve range migration correction and moving target focusing, which yields the optimal output signal and filtering parameters. Finally, constant false alarm rate (CFAR) detection is carried out to determine the targets, and the space-variant Doppler parameters, solved from the filtering parameters, are used for estimating moving target positions and velocities. Simulations verify the effectiveness of our method.

A High-Frequency Motion Error Compensation Algorithm Based on Multiple Errors Separation in BiSAR Onboard Mini-UAVs

In this article, a high-frequency motion error compensation algorithm for mini-unmanned aerial vehicle (UAV)-based bistatic synthetic aperture radar (BiSAR) system is proposed. Compared with the traditional SAR system platform, the mini-UAV is more vulnerable to interference from the external environment. Thus, the motion error of mini-UAV-based BiSAR systems always contains both low- and high-frequency components. Meanwhile, the independent transmitter and receiver sources would introduce more than one high-frequency motion error component, and the variance of the high-frequency motion error also needs to be considered. This article first establishes a high-frequency motion error model and the corresponding Doppler phase signal for the mini-UAV-based BiSAR system. Then, multiple forms and multiple components errors are separated and estimated on the azimuth phase. Next, the complex spatial variance is accurately modeled and compensated by estimating the platforms’ motion errors. It is suggested from the simulation and experimental results that the proposed algorithm can compensate for the motion error with complex forms under the mini-UAV-based BiSAR system condition.

SAR Imaging Method for Moving Targets Based on Omega-k and Fourier Ptychographic Microscopy

The performance of traditional SAR imaging methods is limited by the system bandwidth, and the imaging results are easily affected by the target motion state. In the process of SAR imaging, the relative motion between the radar and the target will cause the nonlinear phase error of the echo signal, which makes the imaging quality worse. To solve this problem, we propose a high-resolution moving targets SAR imaging method based on Omega-k and Fourier Ptychographic Microscopy (FPM) phase retrieval technology, which realizes phase retrieval by alternating iteration between time domain and 2-D frequency domain. The proposed method is mainly divided into two steps. Firstly, reference function multiply and Stolt mapping are performed to realize echo preprocessing in the 2-D frequency domain, and then the phase error correction of the compensated echo spectrum is completed by FPM method. Several groups of simulation data and measured data are used for experimental analysis, and the results verify the effectiveness of the proposed method.

SAR Imaging Method for Moving Target With Azimuth Missing Data

To solve the problem of the moving target SAR imaging from an incomplete echo, we proposed a novel SAR imaging method for the moving target with the azimuth missing data (MTIm-AMD). Instead of directly reconstructing the moving target image using the sparse SAR imaging method, we estimate and reconstruct the non-cooperative moving target&#x0027;s complete echo from the azimuth incomplete echo based on its sparsity. At first, the non-cooperative moving target&#x0027;s motion parameters are estimated from the incomplete echo. Then, to ensure that the complete echo can be well reconstructed using the compressed sensing method, these parameters are exploited to design a phase compensation function. Finally, using the reconstructed data, the fine-focused moving target can be obtained via the traditional SAR imaging algorithm. The simulation and experiment data results verify the effectiveness of the proposed MTIm-AMD method and demonstrate that the moving target can be fine-imaging when the echo SNR is higher than <inline-formula><tex-math notation="LaTeX">$-$</tex-math></inline-formula>20 dB and the azimuth missing ratio (AMR) is less than 70&#x0025;. This implies that the proposed MTIm-AMD method achieves satisfactory robustness to echo SNR and can handle most AMR cases.

Recognition Method of Dim and Small Targets in SAR Images based on Machine Vision

Aiming at the problems of long recognition time and low recognition accuracy of traditional SAR image dim target recognition methods, a method of SAR image dim target recognition based on machine vision was proposed. SAR images are collected and preprocessed by machine vision, and the image information is processed by PCA dimension reduction considering the linear characteristics of the data to extract image features. Then, the SAR image target feature key frame frequency band is divided by the segmentation results, and the recognition model is established based on the image trajectory tracking and target analysis. The proposed algorithm is applied and analyzed. The simulation results show that the proposed algorithm has good recognition rate, average recognition rate and false detection rate are 99% and 0.9%, and can effectively ensure the data processing performance.

An Elevation Ambiguity Resolution Method Based on Segmentation and Reorganization of TomoSAR Point Cloud in 3D Mountain Reconstruction

Tomographic Synthetic Aperture Radar (TomoSAR) is a breakthrough of the traditional SAR, which has the three-dimentional (3D) observation ability of layover scenes such as buildings and high mountains. As an advanced system, the airborne array TomoSAR can effectively avoid temporal de-correlation caused by long revisit time, which has great application in high-precision mountain surveying and mapping. The 3D reconstruction using TomoSAR has mainly focused on low targets, while there are few literatures on 3D mountain reconstruction. Due to the layover phenomenon, surveying in high mountain areas remains a difficult task. Consequently, it is meaningful to carry out the research on 3D mountain reconstruction using the airborne array TomoSAR. However, the original TomoSAR mountain point cloud faces the problem of elevation ambiguity. Furthermore, for mountains with complex terrain, the points located in different elevation periods may intersect. This phenomenon increases the difficulty of solving the problem. In this paper, a novel elevation ambiguity resolution method is proposed. First, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Gaussian Mixture Model (GMM) are combined for point cloud segmentation. The former ensures coarse segmentation based on density, and the latter allows fine segmentation of the abnormal categories caused by intersection. Subsequently, the segmentation results are reorganized in the elevation direction to reconstruct all possible point clouds. Finally, the real point cloud can be extracted automatically under the constraints of the boundary and elevation continuity. The performance of the proposed method is demonstrated by simulations and experiments. Based on the airborne array TomoSAR experiment in Leshan City, Sichuan Province, China in 2019, the 3D model of the surveyed mountain is presented. Moreover, three kinds of external data are applied to fully verify the validity of this method.

Mapping Urban Excavation Induced Deformation in 3D via Multiplatform InSAR Time-Series

Excavation of a subway station and rail crossover cavern in downtown Los Angeles, California, USA, induced over 1.8 cm of surface settlement between June 2018 and February 2019 as measured by a ground-based monitoring system. Point measurements of surface deformation above the excavation were extracted by applying Interferometric Synthetic Aperture Radar (InSAR) time-series analyses to data from multiple sensors with different wavelengths. These sensors include C-band Sentinel-1, X-band COSMO-SkyMed, and L-band Uninhabited Aerial Vehicle SAR (UAVSAR). The InSAR time-series point measurements were interpolated to continuous distribution surfaces, weighted by distance, and entered into the Minimum-Acceleration (MinA) algorithm to calculate 3D displacement values. This dataset, composed of satellite and airborne SAR data from X, C, and L band sensors, revealed previously unidentified deformation surrounding the 2nd Street and Broadway Subway Station and the adjacent rail crossover cavern, with maximum vertical and horizontal deformations reaching 2.5 cm and 1.7 cm, respectively. In addition, the analysis shows that airborne SAR data with alternative viewing geometries to traditional polar-orbiting SAR satellites can be used to constrain horizontal displacements in the North-South direction while maintaining agreement with ground-based data.

Pyrazole-Thiazole Core-Containing Analogs Exhibit Adjunctive Activity with Meropenem against Carbapenem-Resistant Enterobacteriaceae (CRE).

Pyrazole-thiazole core-containing compound KP-40 and 20 novel derivatives were designed and synthesized through traditional SAR analysis. These molecules displayed adjunctive activity with meropenem against Gram-negative bacteria evidenced by a range of fractional inhibitory concentration (FIC=0.5-0.25) and minimum adjunctive concentration (MAC=128-32 μM) values. Of this series of molecules, four compounds displayed notable adjunctive potential, with FIC and MAC values of 0.25 and 32 μM, respectively. Moreover, the solubility of these compounds was improved to an acceptable range. Further analysis using our "in house" permeation and efflux multi parameter optimization (PEMPO) algorithm revealed key physicochemical properties that may be critical for the development of active Gram-negative antibacterials. Taking PEMPO scores into consideration prior to executing synthesis of analogs may be a simple, yet rapid and effective strategy that can be used in conjunction with traditional SAR approaches to aid in the design of potent Gram-negative antibacterials.

Oil Spill Detection of Kerch Strait in November 2007 from Dual-Polarized TerraSAR-X Image Using Artificial and Convolutional Neural Network Regression Models

Baek, W.-K.; Jung, H.-S.; and Kim, D., 2020. Oil spill detection of Kerch strait in November 2007 from dual-polarized TerraSAR-X image using artificial and convolutional neural network regression models. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 137-144. Coconut Creek (Florida), ISSN 0749-0208.The oil spill is the main marine disaster. It is known that the data mining based method performs better in detecting oil than the traditional SAR based method to distinguish from lookalikes. Recently, artificial neural networks were employed to detect the oil spill. For that synthetic aperture radar intensity map, intensity texture map, co-polarized coherence map, co-polarized phase difference texture map, and digital elevation model was employed as input. The detection performance based on data mining largely depends on the used data, the characteristics of the study area, and the used model. Improving detection performance through various existing data mining models is a very important task since minor improvements in detection can have a significant impact on disaster damage mitigation. Artificial and convolutional neural network regression models were applied to detect the oil spill of Kerch strait in November 2007. The two models showed F1-scores of 0.832 and 0.823 respectively. The results of this study would contribute to monitor oil spill dynamic and mitigate damages caused by the oil spill.

An efficient and accurate three-dimensional imaging algorithm for forward-looking linear-array sar with constant acceleration based on FrFT

Linear-array synthetic aperture radar can break through the limitations of traditional SAR on 3-D forward looking imaging, which continues to attract the worldwide attention, especially on 3-D topographic mapping and aircraft blind landing. To overcome the large range migration and strong space-variant characteristic caused by the uniformly accelerated SAR platform, a new 3-D fractional chirp scaling algorithm (FrCSA) for forward looking linear-array SAR with constant acceleration is proposed in this paper. Based on the novel geometry configuration and the equivalent hyperbolic signal model, a chirp scaling operation is employed to compensate the range dependent range cell migration (RCM). Meanwhile, the FrFT tool is introduced to achieve much more accurate focusing. The 3-D imaging results of 3 × 3 × 3 array point scatterers indicate that the proposed method can achieve better focusing capabilities, greater peak side-lobe ratio (PSLR) and integrated side-lobe ratio (ISLR) comparing with the traditional CS algorithm based on FFT.

Power Efficiency Comparison of Event-Driven and Fixed-Rate Signal Conversion and Compression forBiomedical Applications.

Energy-constrained biomedical recording systems need power-efficient data converters and good signal compression in order to meet the stringent power consumption requirements of many applications. In literature today, typically a SAR ADC in combination with digital compression is used. Recently, alternative event-driven sampling techniques have been proposed that incorporate compression in the ADC, such as level-crossing A/D conversion. This paper describes the power efficiency analysis of such level-crossing ADC (LCADC) and the traditional fixed-rate SAR ADC with simple compression. A model for the power consumption of the LCADC is derived, which is then compared to the power consumption of the SAR ADC with zero-order hold (ZOH) compression for multiple biosignals (ECG, EMG, EEG, and EAP). The LCADC is more power efficient than the SAR ADC up to a cross-over point in quantizer resolution (for example 8 bits for an EEG signal). This cross-over point decreases with the ratio of the maximum to average slope in the signal of the application. It also changes with the technology and design techniques used. The LCADC is thus suited for low to medium resolution applications. In addition, the event-driven operation of an LCADC results in fewer data to be transmitted in a system application. The event-driven LCADC without timer and with single-bit quantizer achieves a reduction in power consumption at system level of two orders of magnitude, an order of magnitude better than the SAR ADC with ZOH compression. At system level, the LCADC thus offers a big advantage over the SAR ADC.