Migration Algorithm Research Articles

Joint Physics and Data Driven Full-Waveform Inversion for Underground Dielectric Targets Imaging

To better reconstruct underground targets based on ground-penetrating radar (GPR) data, this paper proposes a joint physics and data driven full-waveform inversion (PDD-FWI) scheme. This scheme combines a physics-based non-iterative approach and a data-driven deep neural network (DNN) to reconstruct target location, shape and permittivity accurately. Firstly, the normalized range migration algorithm (RMA) is introduced to extract the target contour and location information, which not only improves the robustness of the proposed scheme, but also ensures adaptability to different GPR equipment. Then, the GPR dielectric target reconstruction network (GPRDtrNet) is developed based on the improved U-net structure, including reducing network layers and adding multi-scale additive spatial attention gates and skip-connection structures. Compared with previous DNN-based reconstruction methods, GPRDtrNet has the advantages of small data requirement, high accuracy, strong generalization and noise tolerance. Finally, the simulated and real dataset containing kinds of targets is constructed to train and test GPRDtrNet. The results show that the proposed method can reconstruct underground dielectric targets accurately with high robustness and noise tolerance.

Image Defocus in an Airborne UWB VHR Microwave Photonic SAR: Analysis and Compensation

With the exploration of synthetic aperture radar (SAR), the requirements for its functionality, precision, and response time are inevitably increasing, in which the technical core is the generation, reception, and processing of wideband signals with low time cost. Owing to the excellent performance of modern photonics, such as the ultra-wide bandwidth (UWB), flat response, low transmission loss, fast analog signal processing, and microwave photonics (MWP) promises to be an appropriate solution to improve the capability of SAR in resolution, coverage, and efficiency. However, on the one hand, due to the high sensitivity of optical fiber, moisture, temperature, or physical vibration can produce an unknown extra propagation delay. On the other hand, the wavelength shift effect should be taken into consideration for the UWB system. Thereby, under very high resolution (VHR) circumstance, two-dimensional (2-D) defocus including migration through resolution cells (MTRC) and azimuth phase error (APE) becomes a challenge for MWP SAR imaging. Unfortunately, existing 2-D autofocus approaches concerning motion errors inherently fail for the commonly underlying assumption that the prominent nonsystematic residual range cell migration (RCM) is global in the azimuth time domain. In this article, we analyze the effects of the above-mentioned negative factors in the image processing of range migration algorithm (RMA) and reveal the structural characteristics of the 2-D phase errors. A novel two-step postprocessing compensation strategy is developed, and experiments on real data acquired by an airborne MWP SAR system demonstrate its effectiveness.

Numerical study of the imaging capabilities of the pre-Jurassic complex of the Tomsk and Novosibirsk regions

Background and aim. The complexity of the structures of the Paleozoic deposits of Western Siberia requires the use of specialized methods for seismic data processing. However, the standard time processing procedures are still used in Western Siberia. Therefore, in this work, the goal is to study of seismic processing procedures for the construction of high-quality images of the pre-Jurassic complex in Western Siberia. 
 
 Materials and methods. A comparative analysis of time and depth processing was carried out in the paper on realistic synthetic data and models from Western Siberia containing the pre-Jurassic complex. Numerical examples are calculated for synthetic data obtained from two realistic seismic models. To create the first model, various geological and geophysical data from the Tomsk region are used. The most difficult areas of the Paleozoic in this model are steeply dipping carbonate structures and intrusive formations with steep slopes and outcropping to the erosion surface. Another model was built based on the seismic data processing results in the area of the Maloichskoye and Verkh-Tarskoye fields in the Novosibirsk region. Based on these data, the main horizons and a system of sub-vertical faults, characteristic of the pre-Jurassic deposits of the Novosibirsk region, were identified. Seismic data processing was carried out with an emphasis on the possibility of object-oriented migration. 
 
 Results. It is shown that the time processing of seismic data is insufficient and the need for deep processing to construct kinematically correct images of pre-Jurassic deposits. We also compared migration algorithms based on Gaussian beams and found that object-oriented migration gives the best quality results.

A multi-Mobile Agent and optimal itinerary planning-based data aggregation in Wireless Sensor Networks

Due to the energy constraints in Sensor Nodes (SN) and to increase energy efficiency in Data Aggregation (DA) approaches, Wireless Sensor nodes (WSN) requires a suitable network’s life cycle. In order to address the energy efficiency, a new system model is proposed for competent Data Aggregation (DA), and Data processing (DP) mechanism with the amalgamation of Mobile Agent (MA) in WSN intended towards collaborative signal systematized with information processing. Furthermore, with the expansion of Mobile Agent (MA) centred WSN, unique resource-efficient potentials for Data Aggregation (DA) and Data processing (DP) applications are identified. In the proposed system model, a single Mobile Agent (MA) is employed for data processing. Hence the MA-centred paradigm’s fundamental difficulty is determining the best route for agent traversal. Henceforth, using the Brownian Motion-Based Flower Pollination Algorithm (BMFPA), a new multi-MA-centred optimal itinerary planning strategy for performing data aggregation in WSN is proposed. Further, the proposed approach uses Fitness based Fuzzy C-Means (FFCM) for cluster creation and Cluster Head (CH) selection. Crossover Mutation based Firefly Algorithm (CM-FFA) is used. Moreover, the proposed model uses optimal itinerary planning-based Brownian Motion-Based Flower Pollination Algorithm (BMFPA) for MA migration and Data Gathering (DG) in WSN. Finally, after data gathering, the proposed model, the mobile Agent collects the information and communicates it to the base station or sink based on various applications scenarios. The system model’s investigation outcomes evidently exhibit that the proposed work competently performs well than prevailing algorithms for Data Gathering (DG) in WSN. Furthermore, the proposed model method can be fully utilized in a virtual wireless network’s scenario.

Keep Hot or Go Cold: A Randomized Online Migration Algorithm for Cost Optimization in STaaS Clouds

Storage-as-a-Service clouds generally offer both hot and cold storage tiers with different pricing options. Hot tiers provide a higher storage price but a lower access price, and vice versa for cold tiers. Many studies show that those user-generated data generally receive relatively high access frequency in the early period of their lifetimes while the overall trend of accesses is downward. Thus, when such kinds of data are hosted in clouds, they can be stored in hot tiers initially and then migrated to cold tiers for optimizing costs. However, the cost of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">migration</i> is non-negligible, and the number of accesses may then unexpectedly increase after migration, which indicates that a rash migration will incur more costs instead of cost-savings. For making optimal migration decisions, future data access curves are needed, but it is generally very hard to predict them precisely. To solve this problem, in this paper we propose a randomized online algorithm to optimize costs for those user-generated data stored in clouds, without requiring any future information. We show theoretically that the proposed algorithm can achieve a guaranteed competitive ratio of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1 + \frac {2(1-\lambda)}{e - 3 + 2\lambda + \lambda /\alpha }$ </tex-math></inline-formula> , and it can be easily extended with prediction windows when short-term predictions are reliable. Eventually, we validate the effectiveness of our proposed algorithms through simulations driven by real-world video-visiting traces collected from a well-known video-sharing website.

Noninterpolated frequency-domain imaging algorithm for near-field OS-BiSAR in the millimeter-wave band.

This paper presents a frequency-domain imaging algorithm for near-field one-stationary bistatic synthetic aperture radar (OS-BiSAR) in the millimeter-wave (mmW) band. During the image reconstruction process, the interpolation procedure introduced by traditional fast imaging algorithm is avoided. By appropriately decomposing and approximating the nonlinear phase in frequency-domain echo, only multiplication and fast Fourier transform operations are carried out to achieve the lowest computational load. In addition, this method takes the propagation attenuation into consideration, which leads to a better imaging quality than the improved range migration algorithm for distant targets. Both simulation and measurement results validate the effectiveness of the proposed method on high-precision real-time imaging.

Reverse time migration with Gaussian beams using optimized ray tracing systems in transversely isotropic media

ABSTRACTPrestack depth migration is a key technology for imaging complex reservoirs in media with strong lateral velocity variations. Prestack migrations are broadly separated into ray‐based and wave‐equation‐based methods. Because of its efficiency and flexibility, ray‐based Kirchhoff migration is popular in the industry. However, it has difficulties in dealing with the multi‐arrivals, caustics and shadow zones. On the other hand, wave‐equation‐based methods produce images superior to that of the ray‐based methods, but they are expensive numerically, especially methods based on two‐way propagators in imaging large regions. Therefore, reverse time migration algorithms with Gaussian beams have recently been proposed to reduce the cost, as they combine the high computational efficiency of Gaussian beam migration and the high accuracy of reverse time migration. However, this method was based on the assumption that the subsurface is isotropic. As the acquired azimuth and maximum offsets increase, taking into account the influence of anisotropy on seismic migration is becoming more and more crucial. Using anisotropic ray tracing systems in terms of phase velocity, we proposed an anisotropic reverse time migration using the Gaussian beams method. We consider the influence of anisotropy on the propagation direction and calculate the amplitude of Gaussian beams with optimized correlation coefficients in dynamic ray tracing, which simplifies the calculations and improves the applicability of the proposed method. Numerical tests on anisotropic models demonstrate the efficiency and accuracy of the proposed method, which can be used to image complex structures in the presence of anisotropy in the overburden.

Low-latency edge cooperation caching based on base station cooperation in SDN based MEC

With the increase of mobile terminal equipment and network mass data, users have higher requirements for delay and service quality. To reduce user access latency and more effectively cache diverse content in the edge network, a low-latency edge caching method is proposed. The cache model based on base station cooperation is established and the delay in different transmission modes is considered. Finally, the problem of minimizing latency is transformed into a problem of maximizing cache reward, and a greedy algorithm based on the original dual interior point is used to obtain the strategy of the original problem. Meanwhile, in order to improve service quality and balance communication overhead and migration overhead, a migration method based on balanced communication overhead and migration overhead is proposed. The model that balances communication overhead and migration overhead is established, and the reinforcement learning method is used to obtain a migration scheme that maximizes accumulated revenue. Comparison results show that our caching method can enhance the cache reward and reduce delay. Meanwhile, the migration algorithm can increase service migration revenue and reduce communication overhead.

CIMAR, NIMAR, and LMMA: Novel algorithms for thread and memory migrations in user space on NUMA systems using hardware counters

This paper introduces two novel algorithms for thread migrations, named CIMAR (Core-aware Interchange and Migration Algorithm with performance Record –IMAR–) and NIMAR (Node-aware IMAR), and a new algorithm for the migration of memory pages, LMMA (Latency-based Memory pages Migration Algorithm), in the context of Non-Uniform Memory Access (NUMA) systems. This kind of system has complex memory hierarchies that present a challenging problem in extracting the best possible performance, where thread and memory mapping play a critical role. The presented algorithms gather and process the information provided by hardware counters to make decisions about the migrations to be performed, trying to find the optimal mapping. They have been implemented as a user space tool that looks for improving the system performance, particularly in, but not restricted to, scenarios where multiple programs with different characteristics are running. This approach has the advantage of not requiring any modification on the target programs or the Linux kernel while keeping a low overhead.Two different benchmark suites have been used to validate our algorithms: The NAS parallel benchmark, mainly devoted to computational routines, and the LevelDB database benchmark focused on read–write operations. These benchmarks allow us to illustrate the influence of our proposal in these two important types of codes. Note that those codes are state-of-the-art implementations of the routines, so few improvements could be initially expected. Experiments have been designed and conducted to emulate three different scenarios: a single program running in the system with full resources, an interactive server where multiple programs run concurrently varying the availability of resources, and a queue of tasks where granted resources are limited. The proposed algorithms have been able to produce significant benefits, especially in systems with higher latency penalties for remote accesses. When more than one benchmark is executed simultaneously, performance improvements have been obtained, reducing execution times up to 60%. In this kind of situation, the behaviour of the system is more critical, and the NUMA topology plays a more relevant role. Even in the worst case, when isolated benchmarks are executed using the whole system, that is, just one task at a time, the performance is not degraded.

Imaging Complex Subsurface Structures for Geothermal Exploration at Pirouette Mountain and Eleven-Mile Canyon in Nevada

Accurate imaging of subsurface complex structures with faults is crucial for geothermal exploration because faults are generally the primary conduit of hydrothermal flow. It is very challenging to image geothermal exploration areas because of complex geologic structures with various faults and noisy surface seismic data with strong and coherent ground-roll noise. In addition, fracture zones and most geologic formations behave as anisotropic media for seismic-wave propagation. Properly suppressing ground-roll noise and accounting for subsurface anisotropic properties are essential for high-resolution imaging of subsurface structures and faults for geothermal exploration. We develop a novel wavenumber-adaptive bandpass filter to suppress the ground-roll noise without affecting useful seismic signals. This filter adaptively exploits both characteristics of the lower frequency and the smaller velocity of the ground-roll noise than those of the signals. Consequently, this filter can effectively differentiate the ground-roll noise from the signal. We use our novel filter to attenuate the ground-roll noise in seismic data along five survey lines acquired by the U.S. Navy Geothermal Program Office at Pirouette Mountain and Eleven-Mile Canyon in Nevada, United States. We then apply our novel anisotropic least-squares reverse-time migration algorithm to the resulting data for imaging subsurface structures at the Pirouette Mountain and Eleven-Mile Canyon geothermal exploration areas. The migration method employs an efficient implicit wavefield-separation scheme to reduce image artifacts and improve the image quality. Our results demonstrate that our wavenumber-adaptive bandpass filtering method successfully suppresses the strong and coherent ground-roll noise in the land seismic data, and our anisotropic least-squares reverse-time migration produces high-resolution subsurface images of Pirouette Mountain and Eleven-Mile Canyon, facilitating accurate fault interpretation for geothermal exploration.

Range-Doppler Spectrograms-Based Clutter Suppression of HF Passive Bistatic Radar by D-CycleGAN

As a new attempt to reject clutter in High Frequency Passive Bistatic Radar (HFPBR), the Cycle-Consistent Adversarial Network (CycleGAN)-based image domain clutter suppression algorithm works successfully by treating the clutter rejection problem as an image-to-image migration between the range-doppler spectrograms without and with clutter suppression. However, this algorithm also has some disadvantages such as too many calculation parameters, difficult model training and low clutter suppression rate. In this paper, a novel clutter suppression method based on an improved DenseNet CycleGAN (D-CycleGAN) is proposed. In this method, the ResNet block of the generator in the original CycleGAN network is replaced with the DenseNet block, to better extract clutter features. To better evaluate the clutter suppression performance of the deep learning network-based graph-relational method, a new index suitable for HF passive radar is introduced. Furthermore, the influence of the number of Dense blocks on the performance of the algorithm is also discussed. Finally, the performance of the proposed method is tested and verified on the measured data, compared with the traditional clutter suppression method and other popular image migration algorithms, such as Pix2pix. The results show that the D-CycleGAN-based method has better clutter suppression performance, stronger generalization ability, and better retained radar targets simultaneously.

MIMO-SAR 3-D Imaging Based on Range Wavenumber Decomposing

Multiple-input-multiple-output synthetic aperture radar (MIMO-SAR) technology is an effective scheme to achieve there-dimensional (3-D) imaging in millimeter wave and terahertz band. Compared with the 2-D array, the system complexity and the number of array elements are greatly reduced. In this paper, an efficient image reconstruction algorithm in wavenumber domain based on phase shift migration (PSM) was proposed for MIMO-SAR 3-D real-time imaging application. By decomposing the range-wavenumber based on binomial approximation theory, the PSM operation can be achieved only by matrix multiplication and Fourier transform (FT) algorithm without time-consuming wavenumber accumulation. Proof-of-principle experiments were also carried out in 0.1THz band based on a MIMO-SAR prototype system. Both simulation and experimental results of different targets show that the image quality of the proposed algorithm is comparable to that of back-projection algorithm (BPA). The partial enlarged images of the imaging results validate that compared with the range migration algorithm (RMA) the proposed algorithm can achieve higher reconstruction accuracy with comparable efficiency.

Ground Moving Target Imaging for Highly Squint SAR by Modified Minimum Entropy Algorithm and Spectrum Rotation

Ground moving target (GMT) is displaced and defocused in conventional synthetic aperture radar (SAR) image due to the residual phase error of non-cooperative GMT motion. In this study, a GMT imaging (GMTIm) method is proposed for highly squint SAR. As the squint angle become large, the displace and defocus effect of the GMT image become severe and the geometry distortion of the GMT image cannot be ignored. The proposed method first deduced the two-dimensional (2-D) frequency domain signal of the GMT and the bulk compression function of the Range Migration Algorithm (RMA) in highly squint SAR. Then GMT ROI data are extracted and a modified minimum entropy algorithm (MMEA) is proposed to refocus the GMT image. MMEA introduces the idea of bisection into the iteration process to converge more efficiently than the previous minimum entropy method. To overcome the geometry distortion of the GMT image, an equivalent squint angle spectrum rotation method is proposed. Finally, to suppress the GMT image sidelobe, the sparse characteristic of GMT is considered and a sparse enhancement method is adopted. The proposed method can realize GMTIm in highly squint SAR where the squint angle reaches to 75 degrees. The PSNR and ISLR of point target in highly squint SAR is close to that in side-looking SAR. The simulated point target data and ship data are used to validate the effectiveness of the proposed method.

Novel Static Multi-Layer Forest Approach and Its Applications

The existing multi-layer tree is of dynamic linked list type which has many limitations and is complicated due to the pointer-node structure. Static array representation gives more flexibility in programming of algorithms and operations like insertion, deletion, and search. It also reduces the storage space. This paper presents a new method for representing multi-layer forest data structure in array format. It also explains various tree operations, unique data compression algorithm and migration algorithm between traditional approach and the proposed data structure. Most of the fundamental algorithms like those from artificial intelligence that employ decision trees are based on trees/forest data structure. The current paper brings a completely new idea in the representation of these data structures without employing recursion and targeting memory optimizations with reduced code complexities. The applications of forest data structures are many and span over various interdisciplinary areas of Engineering, Medicine, Aviation, Locomotive, Marine, etc. The proposed novel approach not just introduces a new method to look at the tree data structure but also provides the flexibility to adapt to the existing methods as per the user needs. A few such applications in Simulink debugging and the Forest visualisation have been discussed in detail in this paper.

Solver-based gradual type migration

Gradually typed languages allow programmers to mix statically and dynamically typed code, enabling them to incrementally reap the benefits of static typing as they add type annotations to their code. However, this type migration process is typically a manual effort with limited tool support. This paper examines the problem of automated type migration: given a dynamic program, infer additional or improved type annotations. Existing type migration algorithms prioritize different goals, such as maximizing type precision, maintaining compatibility with unmigrated code, and preserving the semantics of the original program. We argue that the type migration problem involves fundamental compromises: optimizing for a single goal often comes at the expense of others. Ideally, a type migration tool would flexibly accommodate a range of user priorities. We present TypeWhich, a new approach to automated type migration for the gradually-typed lambda calculus with some extensions. Unlike prior work, which relies on custom solvers, TypeWhich produces constraints for an off-the-shelf MaxSMT solver. This allows us to easily express objectives, such as minimizing the number of necessary syntactic coercions, and constraining the type of the migration to be compatible with unmigrated code. We present the first comprehensive evaluation of GTLC type migration algorithms, and compare TypeWhich to four other tools from the literature. Our evaluation uses prior benchmarks, and a new set of "challenge problems." Moreover, we design a new evaluation methodology that highlights the subtleties of gradual type migration. In addition, we apply TypeWhich to a suite of benchmarks for Grift, a programming language based on the GTLC. TypeWhich is able to reconstruct all human-written annotations on all but one program.

Online Adaptive Interference-Aware VNF Deployment and Migration for 5G Network Slice

Based on network function virtualization (NFV) and software defined network (SDN), <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">network slicing</i> is proposed as a new paradigm for building service-customized 5G network. In each network slice, service-required virtual network functions (VNFs) can be flexibly deployed in an on-demand manner, which will support a variety of 5G use cases. However, due to the real-time network variations and diverse performance requirements among different 5G scenarios, online adaptive VNF deployment and migration are needed to dynamically accommodate to service-specific requirements. In this paper, we first propose a time-slot based 5G network slice model, which jointly includes both edge cloud servers and core cloud servers. Since VNF consolidation may cause severe performance degradation, we adopt a demand-supply model to quantify the VNF interference. To achieve our objective—maximizing the total reward of accepted requests (i.e., the total throughput minus the weighted total VNF migration cost), we propose an Online Lazy-migration Adaptive Interference-aware Algorithm (OLAIA) for real-time VNF deployment and cost-efficient VNF migration in a 5G network slice, where an Adaptive Interference-aware Algorithm (AIA) is proposed as OLAIA’s core function for placing a given set of requests’ VNFs with maximized total throughput. Through trace-driven evaluations on two typical 5G network slices, we demonstrate that OLAIA can efficiently handle the real-time network variations and the VNF interference when deploying VNFs for real-time arriving requests. In particular, OLAIA improves the total reward by 22.18% in the autonomous driving scenario and by 51.10% in the 4K/8K HD video scenario, as compared with other state-of-the-art solutions.

Intelligent Deblending of Seismic Data Based on U-Net and Transfer Learning

The blended acquisition allows multiple sources to be simulated simultaneously in a narrow time interval, which can improve the acquisition efficiency and reduce the acquisition cost tremendously. However, the overlapped information from multiple sources poses challenges for traditional seismic data migration or inversion algorithms. Thus, accurate and efficient deblending should be implemented as a pre-requisite. Traditional inversion-based deblending algorithms can provide deblended data with a high computational burden, especially for a large volume of seismic data. As a deep learning strategy can match seismic data accurately in a nonlinear way through supervised learning, we propose a U-net-based accurate deblending algorithm, which incorporates transfer learning and an iterative strategy. A set of labeled synthetic data with a blending fold of 2 are classified into the training and validation data for U-net training and validation. Field data are regarded as the test data to assess the performance of the trained U-net. To guarantee the deblending performance of the field data to some extent, parts of field data with labels are used to fine-tune the trained U-net based on transfer learning. The fine-tuning procedure is relatively fast within several minutes. To further improve the deblending performance, we incorporate an iterative strategy with the fine-tuned U-net. The deblending performance is promising in the quality and computational efficiency compared with the curvelet-thresholding-based deblending method, which demonstrates the validity of the proposed intelligent deblending method.

PFHA: A Novel Page Migration Algorithm for Hybrid Memory Embedded Systems

Phase change memory (PCM) and DRAM-based hybrid main memory have emerged as one of the most promising technology for the future embedded systems. Previous research works have focused on page migration strategies to fully exploit the advantages of DRAM and PCM to achieve higher performance and energy efficiency. However, the ability to predict the pages’ future behavior in these strategies could be further improved and most strategies do not consider how to reduce the DRAM energy consumption to further decrease the hybrid system energy. In this article, we propose a novel dynamic hardware migration strategy named periodical frequency hot-region aware page replacement algorithm (PFHA), which utilizes the relation of periodical read and write access frequencies to predict the hotness of a certain page. Meanwhile, PFHA innovatively initiates write hot page self-migration within DRAM to establish a concentrate DRAM write hot region to reduce the DRAM energy. Experiment results confirm that the proposed technique is able to make most of the write hot pages absorbed by DRAM. Meanwhile, it decreases the PCM average access time to a great extent and gets preferable energy consumption.

A Modified Range Migration Algorithm for FMCW SAR Signal Processing

The range migration algorithm (RMA) is an accurate imaging method for processing synthetic aperture radar (SAR) signals. However, this algorithm requires a big amount of computation when performing Stolt mapping. In high squint and wide beamwidth imaging, this operation also requires big memory size to store the result spectrum after Stolt mapping because the spectrum will be significantly expanded. A modified Stolt mapping that does not expand the signal spectrum while still maintains the processing accuracy is proposed in this paper to improve the efficiency of the RMA when processing frequency modulated continuous wave (FMCW) SAR signals. The modified RMA has roughly the same computational load and required the same memory size as the range Doppler algorithm (RDA) when processing FMCW SAR data. In extreme cases when the original spectrum is significantly modified by the Stolt mapping, the modified RMA achieves better focusing quality than the traditional RMA. Simulation and real data is used to verify the performance of the proposed RMA.

Staring Spotlight SAR with Nonlinear Frequency Modulation Signal and Azimuth Non-Uniform Sampling for Low Sidelobe Imaging

In synthetic aperture radar (SAR) imaging, geometric resolution, sidelobe level (SLL) and signal-to-noise ratio (SNR) are the most important parameters for measuring the SAR image quality. The staring spotlight mode continuously transmits signals to a fixed area by steering the azimuth beam to acquire azimuth high geometric resolution, and its two-dimensional (2D) impulse response with the low SLL is usually obtained from the 2D weighted power spectral density (PSD) by the selected weighting window function. However, this results in the SNR reduction due to 2D amplitude window weighting. In this paper, the staring spotlight SAR with nonlinear frequency modulation (NLFM) signal and azimuth non-uniform sampling (ANUS) is proposed to obtain high geometric resolution SAR images with the low SLL and almost without any SNR reduction. The NLFM signal obtains non-equal interval frequency sampling points under uniform time sampling by adjusting the instantaneous chirp rate. Its corresponding PSD is similar to the weighting window function, and its pulse compression result without amplitude window weighting has low sidelobes. To obtain a similar Doppler frequency distribution for low sidelobe imaging in azimuth, the received SAR echoes are designed to be non-uniformly sampled in azimuth, in which the sampling sequence is dense in middle and sparse in both ends, and azimuth compression result with window weighting would also have low sidelobes. According to the echo model of the proposed imaging mode, both the back projection algorithm (BPA) and range migration algorithm (RMA) are modified and presented to handle the raw data of the proposed imaging mode. Both imaging results on simulated targets and experimental real SAR data processing results of a ground-based radar validate the proposed low sidelobe imaging mode.