Requirements Of Real-time Systems Research Articles

OMNeT++ Simulation Framework for Avionics Full-Duplex Switched Ethernet

Avionics Full-Duplex Switched Ethernet (AFDX) is a popular network technology for integrated modular avionics (IMA). When designing an AFDX network, it is important to fulfill the real-time communication requirements of avionic systems. To this end, simulation tools offer a low-cost solution for the performance evaluation of a given AFDX network and message set configuration at the design stage. In this paper, we develop an improved AFDX model for the widely used OMNeT++ (extensible, modular, component-based C++ simulation library and framework) network simulator. Our model exhibits high fidelity to the standard, adds detailed parameter configuration and data logging capabilities, and is compatible with the most recent OMNeT++ version. Our model is accepted by the OMNeT++ community and published as the current AFDX simulation model. Moreover, we propose a complete AFDX simulation framework by augmenting our improved OMNeT++ models with a new network configuration and analysis software tool that we call ANCAT. ANCAT enables users who lack familiarity with the OMNeT++ interface and specific file formats to provide simulation parameters in generic file formats such as.xlsx. Subsequently, ANCAT runs the simulation and generates reports for the results. We first systematically verify the correctness of our AFDX simulation framework by custom-designed experiments. Then, we investigate the practicability of our proposed framework with the performance analysis of a realistic AFDX network topology and message set. In particular, we demonstrate that our proposed framework can be efficiently used to explore the real-time capabilities and relevant features of AFDX networks, as well as provide basic guidelines for network configuration.

Real-time diabetic foot ulcer classification based on deep learning & parallel hardware computational tools

Meeting the rising global demand for healthcare diagnostic tools is crucial, especially with a shortage of medical professionals. This issue has increased interest in utilizing deep learning (DL) and telemedicine technologies. DL, a branch of artificial intelligence, has progressed due to advancements in digital technology and data availability and has proven to be effective in solving previously challenging learning problems. Convolutional neural networks (CNNs) show potential in image detection and recognition, particularly in healthcare applications. However, due to their resource-intensiveness, they surpass the capabilities of general-purpose CPUs. Therefore, hardware accelerators such as application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), and graphics processing units (GPUs) have been developed. With their parallelism efficiency and energy-saving capabilities, FPGAs have gained popularity for DL networks. This research aims to automate the classification of normal and abnormal (specifically Diabetic Foot Ulcer—DFU) classes using various parallel hardware accelerators. The study introduces two CNN models, namely DFU_FNet and DFU_TFNet. DFU_FNet is a simple model that extracts features used to train classifiers like SVM and KNN. On the other hand, DFU_TFNet is a deeper model that employs transfer learning to test hardware efficiency on both shallow and deep models. DFU_TFNet has outperformed AlexNet, VGG16, and GoogleNet benchmarks with an accuracy 99.81%, precision 99.38% and F1-Score 99.25%. In addition, the study evaluated two high-performance computing platforms, GPUs and FPGAs, for real-time system requirements. The comparison of processing time and power consumption revealed that while GPUs outpace FPGAs in processing speed, FPGAs exhibit significantly lower power consumption than GPUs.

Real-Time Recognition of Molten Pools Based on Improved DeepLabV3+ in Keyhole Tungsten Inert Gas Welding Applications

During the Keyhole Tungsten Inert Gas (K-TIG) welding process, a significant amount of information related to the weld quality can be obtained from the weld pool and the keyhole of the topside molten pool image, which provides a vital basis for the control of welding quality. However, the topside molten pool image has the unstable characteristic of strong arc light, which leads to difficulty in contour extraction. The existing image segmentation algorithms cannot satisfy the requirements for accuracy, timing, and robustness. Aiming at these problems, a real-time recognition method, based on improved DeepLabV3+, for identifying the molten pool more accurately and effectively was proposed in this paper. First, MobileNetV2 was selected as the feature extraction network with which to improve detection efficiency. Then, the atrous rates of atrous convolution layers were optimized to reduce the receptive field and balance the sensitivity of the model to molten pools of different scales. Finally, the convolutional block attention module (CBAM) was introduced to improve the segmentation accuracy of the model. The experimental results verified that the proposed model had a fast segmentation speed and higher segmentation accuracy, with an average intersection ratio of 89.89% and an inference speed of 103 frames per second. Furthermore, the trained model was deployed in a real-time system and achieved a real-time performance of up to 28 frames per second, thus meeting the real-time and accuracy requirements of the K-TIG molten pool monitoring system.

Edge Server Deployment for Health Monitoring With Reinforcement Learning in Internet of Medical Things

The Internet of Medical Things (IoMT) has recently gained a lot of interest in the health care industry. IoMT enables real-time and omnipresent monitoring of a patient's health status, resulting in massive amounts of medical data being generated. The centralized massive data processing places enormous strain on the typical cloud computing, rendering it incapable of supporting a variety of real-time health care applications. Therefore, edge computing that moves application programs and data processing from central infrastructure to the edge nodes has attracted wide attention. However, adopting existing edge server (ES) deployment strategies for IoMT is not suitable due to the decentralized and high real-time service requirements of IoMT systems. In particular, traditional ES deployment strategies in IoMT system confront major load imbalance across ESs, latency issues, and energy consumption concerns. To address these challenges, a deployment strategy of ESs based on the state-action-reward-state-action (SARSA) learning, named ESL, is designed. Specifically, ESs are quantified by evaluating the silhouette coefficient (SC) and the sum of squared errors. Then, through fuzzy C-means (FCM) algorithm, the preliminary division of health monitoring units (HMUs) and the initial locations of ESs are obtained. Finally, SARSA learning is adopted to determine the deployment of ESs. Furthermore, extensive experiments and analyses confirm that ESL achieves the core objective of optimizing load balancing among ESs while also optimizing request-response latency and request processing energy consumption.

HYBRID DEEP LEARNING APPROACH FOR VEHICLE’S RELATIVE ATTITUDE ESTIMATION USING MONOCULAR CAMERA

Abstract. Relative pose estimation using a monocular camera is one of the most common approaches for aiding vehicle’s navigation. It involves determining the position and orientation of a vehicle relative to its surroundings using only a single camera. This can be achieved through four main steps: feature detection and matching, motion estimation, filtering and optimization, and scale estimation. Feature tracking involves detecting and tracking distinctive visual features in the environment, such as corners or edges, and using their relative motion to estimate the camera's movement. This approach can be prone to errors due to feature detection and tracking difficulties, as well as issues with moving objects, occlusions, and changes in lighting conditions. These typical computer vision approaches are computationally intensive and may require significant processing power as well, which limits their real time application. This paper proposes a hybrid deep neural network approach for estimating the relative attitude of a vehicle using a monocular camera to aid in vehicle navigation. The proposed neural network adopts a relatively shallow architecture to minimize the computational cost and to meet the real-time requirements of low-cost processing systems. The network is trained using the KITTI dataset and can estimate the relative attitude of the vehicle with a RMSE of relative orientation of 0.017 degrees per frame. The processing time of the proposed approach is around 28 ms per frame including both the tracking and network prediction steps, which is significantly faster than the typical estimation pipelines. The results show that the proposed approach is a viable alternative to conventional computer vision methods and can significantly reduce computational costs, deal with the confusing scenarios of the moving objects while maintaining a good accuracy in estimating ego-motion.

Spatial constraint for efficient semi-supervised video object segmentation

Semi-supervised video object segmentation is the process of tracking and segmenting objects in a video sequence based on annotated masks for one or more frames. Recently, memory-based methods have attracted a significant amount of attention due to their strong performance. Having too much redundant information stored in memory, however, makes such methods inefficient and inaccurate. Moreover, a global matching strategy is usually used for memory reading, so these methods are susceptible to interference from semantically similar objects and are prone to incorrect segmentation. We propose a spatial constraint network to overcome these problems. In particular, we introduce a time-varying sensor and a dynamic feature memory to adaptively store pixel information to facilitate the modeling of the target object, which greatly reduces information redundancy in the memory without missing critical information. Furthermore, we propose an efficient memory reader that is less computationally intensive and has a smaller footprint. More importantly, we introduce a spatial constraint module to learn spatial consistency to obtain more precise segmentation; the target and distractors can be identified by the learned spatial response. The experimental results indicate that our method is competitive with state-of-the-art methods on several benchmark datasets. Our method also achieves an approximately 30 FPS inference speed, which is close to the requirement for real-time systems.

An extremum-guided interpolation for sparsely sampled photoacoustic imaging

In photoacoustic (PA) reconstruction, spatial constraints or real-time system requirements often result to sparse PA sampling data. For sparse PA sensor data, the sparse spatial and dense temporal sampling often leads to poor signal continuity. To address the structural characteristics of sparse PA signals, a data interpolation algorithm based on extremum-guided interpolation is proposed. This algorithm is based on the continuity of the signal, and can complete the estimation of high sampling rate signals without complex mathematical calculations. PA signal data is interpolated and reconstructed, and the results are evaluated using image quality assessment methods. The simulation and experimental results show that the proposed method performs better than several typical algorithms, effectively restoring image details, suppressing the generation of artifacts and noise, and improving the quality of PA reconstruction under sparse sampling.

Research on Voltage Sag Detection Algorithm based on Regional Transmission System

Voltage sag is the most common power quality issue and often brings huge economic losses. To achieve voltage sag prevention and compensation, it is necessary to effectively detect voltage sag characteristics. An IEEE-39 node system model is built using Simulink, combined with common aer transformation methods and αβ. The transformation method accurately detects voltage sag accidents and incorporates a derivative algorithm, greatly reducing the detection delay and meeting the real-time and accuracy requirements of actual systems.

Investigation of Campbell mode with automatic calibration operated in fission chamber-based neutron flux monitoring system on EAST

To satisfy the wide-range requirement for the fission chamber-based neutron flux monitoring (NFM) system on experimental advanced superconducting tokamak (EAST), the pulse-counting and Campbell modes were combined to expand the measurement range. However, to obtain the neutron yields, the results of the Campbell mode should first be converted into neutron count rates via linear calibration. During the plasma experiment, the non-neutron backgrounds vary with respect to different experimental conditions, which leads to a baseline dynamic change in the Campbell mode. In future research, NFM system need provide real-time feedback parameters regarding the operational status of the fusion device. Therefore, an automatic calibration method was developed to automatically deduct various baselines of the Campbell mode and obtain the neutron yields in real time. This method continuously updates the average value of each sub-interval (multiple sub-intervals are set in the calibration region) using a linear superposition averaging method. In addition, to optimize the dual-mode switching threshold, ensure the calibration region and define the upper limit of Campbell mode, a simulated neutron signal generator was designed and applied. The switching threshold of the dual modes was set to 400 kcps, the calibration region of the Campbell mode was selected as 50–500 kcps, and the upper limit of the equivalent count rate in the Campbell mode reached to 108cps with a relative error of<4%. Finally, the automatic calibration method was tested using experimental data from the NFM system on EAST. The automatic calibration results basically overlapped with the reference results and superior to the offline fixed calibration results. Under different experimental conditions, the relative root mean square error (RRMSE) values of the automatic calibration results were lower than those of the offline fixed calibration results. Therefore, the proposed automatic calibration method is accurate and reliable, and can satisfy the real-time measurement requirements of wide-range NFM system.

Sample Reduction-Based Pairwise Linear Regression Classification for IoT Monitoring Systems

At present, the development of the Internet of Things (IoT) has become a significant symbol of the information age. As an important research branch of it, IoT-based video monitoring systems have achieved rapid developments in recent years. However, the mode of front-end data collection, back-end data storage and analysis adopted by traditional monitoring systems cannot meet the requirements of real-time security. The currently widely used edge computing-based monitoring system can effectively solve the above problems, but it has high requirements for the intelligent algorithms that will be deployed at the edge end (front-end). To meet the requirements, that is, to obtain a lightweight, fast and accurate video face-recognition method, this paper proposes a novel, set-based, video face-recognition framework, called sample reduction-based pairwise linear regression classification (SRbPLRC), which contains divide SRbPLRC (DSRbPLRC), anchor point SRbPLRC (APSRbPLRC), and attention anchor point SRbPLRC (AAPSRbPLRC) methods. Extensive experiments on some popular video face-recognition databases demonstrate that the performance of proposed algorithms is better than that of several state-of-the-art classifiers. Therefore, our proposed methods can effectively meet the real-time and security requirements of IoT monitoring systems.

Intelligent Seam Tracking of an Ultranarrow Gap During K-TIG Welding: A Hybrid CNN and Adaptive ROI Operation Algorithm

Deep-penetration keyhole tungsten inert gas (K-TIG) welding uses a large current for welding. The extraction of the deviation between the position of the welding torch and the centerline of the weld is the prerequisite for the realization of seam tracking. However, K-TIG welded workpieces are often assembled without a groove and the gap between each weld is super narrow (0.2–1 mm), making it difficult to accurately identify the seam. In addition, traditional methods of identifying seams based on a fixed region of interest (ROI) are likely to lead to seam tracking failures when the type of weld changes. A narrow gap seam tracking algorithm suitable for different weld shapes is developed to address these factors and increase the robustness. To minimize the interference caused by strong arc lights, the welding images are captured by an high dynamic range (HDR) camera. A you only look once (YOLOv5)-based object detection algorithm is used to determine the position of the torch by the center of the keyhole entrance. Then, an image processing algorithm based on the adaptive ROI operation is used to adaptively extract the weld centerline and obtain the weld deviations. The experimental results proved that the welding deviation detected by the algorithm fluctuated within ±0.122 mm and that the average error was within ±0.043 mm. Moreover, the real-time performance of the algorithm can reach up to 45 frames per second (FPS), which is sufficient to meet the real-time and accuracy requirements of the K-TIG seam tracking system.

Flexible Real-Time Operating System Co-Design Flow for Embedded Computing Systems

The adaptability of software portability and the strength of hardware efficiency can both benefit a system with the ability to alter its configuration. This signifies a significant shift in the way embedded programmes are conceived about and developed. There has not been a comprehensive design methodology provided for run-time reconfigurable systems that enable real-time operating systems, despite the fact that many different reconfigurable technologies and tools based on those technologies have been developed. The RTOS, or real-time operating system, is an integral part of system and co-design. A new co-design technique to meet the requirements of real-time operating systems on reconfigurable embedded systems is proposed based on the findings of this study. Co-design techniques needed to create an adaptive signal filtering system on a commercially available reconfigurable platform are highlighted in this study’s presentation of a design scenario. The incident has been documented on paper. The findings indicate that, in comparison to a purely software

Design and implementation of Pedestrian detection based on Zynq APSoc

Pedestrian recognition is an important content of intelligent security surveillance video processing system. In order to meet the application requirements of miniaturization and real-time security system, a moving target recognition system based on APSoc platform is designed. The system uses ov5640 camera as the video collector, zynq7020 as the development platform, and uses the soft and hard co-design to realize each function module. Finally, the pedestrian image acquisition and real-time display system is successfully completed. The test results show that the system can effectively and stably track pedestrians and moving objects within a certain range of distance, and can be displayed in real time, miniaturized and low power consumption, which can be further applied in the field of security.

Online health assessment method based on belief rule base with sliding time window considering input correlation and redundancy

This paper develops a new health assessment method based on belief rule base with sliding time window considering correlation and redundancy of input information (BRB-WCR). The new proposed method aims to solve four problems in health assessment of dynamic complex system: lack of system fault data, uncertainty of expert knowledge, correlation of characteristic and high real-time requirement of system. The traditional BRB model can address the first two problems while it assumes that the input characteristics are completely independent. In order to solve the two remaining problems, a new BRB-WCR model is developed, where the correlation and redundancy of characteristics are considered and the sliding time window for online training data is introduced to improve the real time performance of the model. Moreover, to address the uncertainty of expert knowledge, a new optimization model is developed to train the parameters of the BRB-WCR model. To illustrate the effectiveness of the developed method, a case study of inertial navigation platform is conducted.

Towards a Tractable Exact Test for Global Multiprocessor Fixed Priority Scheduling

Scheduling algorithms are called “global” if they can migrate tasks between cores. Global scheduling algorithms are the de-facto standard practice for general purpose Operating Systems, to balance the workload between cores. However, the exact schedulability analysis of real-time applications for these algorithms is proven to be weakly NP-hard. Despite such a hardness, the research community keeps investigating the methods for an exact schedulability analysis for its relevance and to tightly estimate the execution requirements of real-time systems. Due to the NP-hardness, the available exact tests are very time and memory demanding even for sets of a few tasks. On another hand, the available sufficient tests are very pessimistic, despite consuming less resources. Motivated by these observations, we propose an exact schedulability test for constrained-deadline sporadic tasks under global multiprocessor fixed-priority scheduling scheduler, which is significantly faster and consumes less memory, compared to any other available exact test. To derive a faster test, we exploit the idea of a state-space pruning, aiming at reducing the number of feasible system states to be examined by the test. The resulted test is multiple orders of magnitude faster with respect to other state-of-the-art exact tests. Our C++ implementation is publicly available.

ADAPTIVE MODELS OF ELECTRIC LOAD FORECASTING OF PRODUCTION CONSUMERS

The article investigates models and methods of electric load forecasting. It is shown that the following methods of power consumption control are currently known: instantaneous norm; at the ideal rate; management on the forecast value; control with the use of average power on a moving time interval ("moving window" method). It is shown that it is better to focus on those methods that are based on the study of forecast estimates, which are the source information for management decisions. The main requirements for real-time systems are: high accuracy of operational forecasting and simplicity of algorithms, which provides a minimum solution time; work in conditions of uncertain and insufficient information, ensuring the stability of management. The analysis of works devoted to the issues of forecasting the processes of power consumption management systems of industrial enterprises is carried out. It is shown that automated control systems have specific requirements for mathematical forecasting methods due to little study of the nature of the forecast parameter, small amount of reporting statistics and insufficient reliability of source information and most accurately meets such requirements, adaptive approach to method design. The adaptive approach allows to solve the problem of adequacy of the method of the object of forecasting and from the point of view of simplicity of realization and time of calculations in the first place it is necessary to put adaptive methods of forecasting and, first of all, the method of exponential smoothing. Exponential smoothing, considered in this paper as a predictive model, allows to identify the inadequacy of the model to the real process and to bring the estimate of the determined basis of the process closer to the real one, ie to reduce the prediction error. However, this requires time, which increases with increasing changes in the coefficients of the model. In this regard, there is a problem of regulating the reaction rate of the predicted model to changes in its coefficients. A number of methods of automatic adjustment of the smoothing parameter are considered and analyzed: the evolutionary method of adaptation, methods using the tracking signal, methods of adapting the parameter by, optimization using gradient smoothing. It is shown that the method of adaptation using the tracking signal is simple and especially valuable for modeling series with a short history. Due to its simplicity, this method is especially convenient where predictions are made using computer technology. Studies of this model of forecasting on statistical data obtained at various enterprises have shown that the model adapts to real data at step 4-6 of forecasting and then the forecast error does not exceed 2%. Analysis of adaptive forecasting models based on the method of exponential smoothing showed their high efficiency and good adaptability to changes in the process of electricity consumption. The greatest difficulty in forecasting are cases of abrupt changes in the development of the process. Abrupt changes in the process can lead to a violation of the previously existing qualitative relationships of the parameters of the projected system. If there is a jump, it is very important to assess whether the deviation is caused by an obstacle or whether it is due to a change in the predicted process. If the changes are caused by an obstacle, it must be filtered out. If the deviations are caused by a change in the model, then the current process data are of the greatest value. From the point of view of fast working off of abrupt change the model of exponential smoothing with high value of the smoothing parameter is rather effective. However, this model is highly susceptible to interference. To eliminate this circumstance, a modified procedure for correcting the parameters of the forecasting model is proposed. The procedure is based on the introduction of a logical operator, which is based on the analysis of inconsistencies in the forecasts and imposes additional restrictions on changes in the smoothing parameter and the values of the original statistics. Experimental studies of the considered models are carried out.

An Adaptive Modeling and Performance Evaluation Framework for Edge-Enabled Green IoT Systems

The enormous growth in Internet of Things (IoT) has caused large-scale transformation in data acquisition and communication mechanism for conventional IoT systems. The continuously increasing requirements for delay-tolerant delivery of services in IoT applications has led to the emergence of more scalable and energy-efficient computing platforms like edge computing. However, the massive growth in volume of data being offloaded from low-powered IoT devices to the edge has imposed challenges on edge servers in terms of traffic bottlenecks, latency, and wastage of energy. In this view, a Local Data Reduction (LDR) framework is proposed which addresses the latency issues and cost constraints to facilitate energy-efficient processing of IoT data. We exploit the Markovian birth-death process to model edge-based IoT systems and derive performance metrics for the proposed LDR model. We also provide explicit analytical solution for the total expected cost function incurred pertaining to the LDR and without LDR (WLDR) models. Through extensive numerical illustrations we validate our findings and observe that the proposed LDR model outperforms the WLDR model. Hence, the LDR model operates well to meet the Quality of Service (QoS) requirements for real-time IoT systems by favouring green computing paradigms.

Long-, Medium-, and Short-Term Nested Optimized-Scheduling Model for Cascade Hydropower Plants: Development and Practical Application

This paper presents a nested approach for generating long-term, medium-term, and short-term reservoir scheduling models, which is based on the actual needs of the scheduling operation of the Three Gorges–Gezhouba (TG-GZB) cascade reservoirs. The approach has established a five-tier optimal scheduling model in which the time interval of the scheduling plan prepared by the model can be as short as 15 min, meeting the real-time scheduling requirements of the cascade hydropower station system. This study also presents a comparatively comprehensive introduction to all solving algorithms that have ever been adopted in the multi-time scale coordinated and optimized scheduling model. Based on that, some practical and efficient solving algorithms are developed for the characteristics of the scheduling model, including the coupled iterative method of alternating reservoirs (CIMAR)—the improved dynamic programming (IDP) algorithm and the improved genetic algorithm (IGA). In addition, optimized-scheduling solutions were generated by each of the three algorithms and were compared in terms of their convergence rate, calculation time, electric energy generated, and standard deviation of the algorithm. The results based on the Cascade Scheduling and Communication System (CSCS) of Three Gorges–Gezhouba, China, which includes two interlinked mega-scale reservoir projects, show that scheduling models have better efficiency and good convergence, and more importantly, the maximization of the power generation benefits of the hydropower plants has been achieved without violating any of the reservoir scheduling regulations.

Intelligent Target Detection and Tracking Algorithm for Martial Arts Applications

Moving object detection and tracking is the basis and key technology for intelligent video surveillance, human-computer interaction, mobile robot and vehicle visual navigation, industrial robot system, and other applications. It has important applications in intelligent monitoring, human-computer interaction, visual navigation, and so on. Intelligent technology can greatly facilitate the monitoring of countless targets. This paper adopts the random motion model to describe the motion state of the target. Based on wireless communication and information security, this paper studies the communication and information security of the intelligent system of martial arts target detection and tracking algorithm, and the basic idea of mean-shift tracking algorithm is to use gradient climbing of probability density to find local optimal. This paper uses the background subtraction method based on the vibe algorithm to obtain the binary image of the foreground object; the shadow detection algorithm is used to remove the shadow of the foreground image; the haar-like (Haar) feature is selected as the feature of motion detection, and the feature value of the rectangular area is quickly calculated to describe the adjacent image area. The difference between the features, the final result image, and then the intelligent system for analysis. Experimental data shows that the time consumed by the tracking algorithm is less than 20 ms, which can meet the real-time requirements of ordinary target tracking systems. The average processing time of the hybrid modeling method is 62.8 ms, and the detection rate is 15.92 frames/SEC. The results show that the algorithm improves the utilization of particles, greatly reduces the complexity, and reduces the degradation of the particle filter.

Parallel Optimisation and Implementation of a Real-Time Back Projection (BP) Algorithm for SAR Based on FPGA.

This study conducts an in-depth evaluation of imaging algorithms and software and hardware architectures to meet the capability requirements of real-time image acquisition systems, such as spaceborne and airborne synthetic aperture radar (SAR) systems. By analysing the principles and models of SAR imaging, this research creatively puts forward the fully parallel processing architecture for the back projection (BP) algorithm based on Field-Programmable Gate Array (FPGA). The processing time consumption has significant advantages compared with existing methods. This article describes the BP imaging algorithm, which stands out with its high processing accuracy and two-dimensional decoupling of distance and azimuth, and analyses the algorithmic flow, operation, and storage requirements. The algorithm is divided into five core operations: range pulse compression, upsampling, oblique distance calculation, data reading, and phase accumulation. The architecture and optimisation of the algorithm are presented, and the optimisation methods are described in detail from the perspective of algorithm flow, fixed-point operation, parallel processing, and distributed storage. Next, the maximum resource utilisation rate of the hardware platform in this study is found to be more than 80%, the system power consumption is 21.073 W, and the processing time efficiency is better than designs with other FPGA, DSP, GPU, and CPU. Finally, the correctness of the processing results is verified using actual data. The experimental results showed that 1.1 s were required to generate an image with a size of 900 × 900 pixels at a 200 MHz clock rate. This technology can solve the multi-mode, multi-resolution, and multi-geometry signal processing problems in an integrated manner, thus laying a foundation for the development of a new, high-performance, SAR system for real-time imaging processing.