Frequency Scaling Algorithm Research Articles

Frequency Domain Imaging Algorithms for Short-Range Synthetic Aperture Radar

In order to achieve miniaturization, short-range radar (SRR) generally adopts millimeter-wave (MMW) radar with a frequency-modulated continuous-wave (FMCW) system, which may make the stop–go–stop assumption in traditional synthetic aperture radar (SAR) imaging algorithms invalid. In addition, in order to observe a large enough area, SRR often needs a wide radar beam, which may cause serious range–azimuth coupling when using SRR for SAR imaging. The above two problems may make the traditional SAR imaging algorithm invalid in SRR SAR imaging. Taking the SRR SAR imaging application into account, traditional frequency domain SAR imaging algorithms are analyzed and improved in this paper. Firstly, the intra-pulse motion (IPM) caused by the FMCW system and the two-dimensional coupling (TDC) in the case of a wide beam are analyzed. Subsequently, the applicability of the range Doppler algorithm (RDA), the frequency scaling algorithm (FSA) and the range migration algorithm (RMA) for SRR SAR is analyzed. Then, improvement measures are put forward to address the aliasing and folding phenomena caused by the wide-beam problem in the FSA and RMA, respectively. Finally, the effectiveness of the proposed algorithm is verified using simulation data and real measured data collected using an MMW radar fixed on a slide rail.

A Fast 3-D Chirp Scaling Imaging Technique for Millimeter-Wave Near-Field Imaging

In this article, a novel 3-D imaging method based on the chirp scaling algorithm (CSA) is proposed for millimeter-wave near-field imaging. We deduce the theoretical formula of CSA applied to multiple-input–multiple-output (MIMO)/single-input–single-output (SISO) array imaging and then analyze the computational complexity and phase error. Compared to the classical Fourier-based algorithm, such as range migration algorithm (RMA), they can replace the redundant interpolation with the phase multiplication. Meanwhile, it can handle the large relative bandwidth system compared with frequency scaling algorithm (FSA). It greatly improves imaging speed and broadens the application scenarios. The rearrangement operation of the MIMO wavenumber spectrum is also accelerated by the sparse matrix multiplication. To further reduce the computation time, we perform the direct rearrangement of the MIMO wavenumber spectrum before phase compensation. It works well but also sacrifices part of the signal-to-noise ratio (SNR), so it may not be suitable for the weakly scattering targets. The simulation data of a 2-D uniform array with the large number of antennas and a published dataset are used to verify the effect of the proposed algorithm. The simulation and experiment results show that the proposed method can effectively improve the computational efficiency.

A Full-Aperture Frequency Scaling Algorithm for Processing Dechirped Sliding Spotlight SAR Data

A Full-Aperture Frequency Scaling Algorithm for Processing Dechirped Sliding Spotlight SAR Data

An Extended Frequency Scaling Algorithm for Bistatic SAR With High Squint Angle

Bistatic synthetic aperture radar (BiSAR) is an extended and complementary observation method to conventional monostatic SAR remote sensing. When high range and azimuth resolution are required in BiSAR, the transmitting platform, i.e., the LEO satellite, should work in the spotlight or sliding spotlight mode, which means a wide bandwidth Chirp signal is transmitted, and the targets are illuminated for a long enough time as well. In order to reduce the sampling frequency in the receiving system and improve the efficiency of the focusing procedure for this BiSAR configuration, the echoes should be dechirp received. A bistatic extended frequency scaling algorithm (Bi-EFSA) is proposed in this paper to deal with the dechirp received bistatic SAR echoes. The first step of Bi-EFSA is the range walk correction in the 2-D time domain, which reduces the space variance of the range curvatures and removals the serious coupling between range and azimuth caused by the high squint angle. After that, the range compression is achieved by secondary range compression (SRC) and bulk shift. Then, the space variance of the azimuth frequency modulation rate, introduced by the range walk correction step, is corrected by implementing the azimuth nonlinear chirp scaling (ANCS) operation. Following this, the focused results in the range frequency and azimuth time domain are obtained by the azimuth compression. The point targets and extended-scene simulations validate the effectiveness of the proposed algorithm.

An Applied Frequency Scaling Algorithm Based on Local Stretch Factor for Near-Field Miniature Millimeter-Wave Radar Imaging

The frequency scaling algorithm (FSA) is a popular imaging algorithm for dechirped SAR data. To obtain a large azimuth detection area, the miniature millimeter-wave (mmW) linear-frequency-modulated continuous-wave (LFMCW) surveillance radar requires a wider azimuth beamwidth, which leads to additional range frequency aliasing in FSA. Because of the adoption of the dechirp-on-receive technique, the sampling frequency is much smaller than the range bandwidth during near-field imaging, which further aggravates the aliasing effects. The target cannot be well focused, and it makes the weak targets submerged in the background. To acquire high-quality SAR images, an improved FSA using the local stretch operation is proposed. The aliasing bandwidth properties introduced by the FS operation and the desired objective range cell migration (RCM) factor are used in this proposed local-stretch FSA (LSFSA). The initial RCM factor is adjusted by the stretch operation to eliminate the frequency aliasing to a certain level without increasing the computing load. The LSFSA is suitable for solving the problem of range frequency aliasing in near-field side-looking SAR and high squint SAR with wide azimuth beamwidth. The proposed method is validated using reasonable simulations and convincing experiments.

An Efficient Algorithm Based on Frequency Scaling for THz Stepped-Frequency SAR Imaging

An efficient algorithm based on the frequency scaling algorithm (FSA) for terahertz (THz) stepped-frequency (SF) synthetic aperture radar (SAR) imaging is proposed in this article. THz SF SAR has an urgent application demand in close-range medical imaging, close-range security check, and near-space military target reconnaissance. In these fields, the real-time performance of the imaging algorithm is of great significance. The back projection algorithm (BPA) is an accurate time-domain imaging algorithm, but it has a huge amount of calculation and cannot meet the requirements of real-time imaging. The frequency-domain SF SAR imaging algorithms are based on interpolation, which will cause Gibb’s phenomenon. An efficient THz SF SAR imaging algorithm named SF-FSA is proposed in this article, which uses the frequency scaling operation and phase multiplication to complete precise range cell migration correction (RCMC), thereby avoiding Gibb’s phenomenon caused by interpolation while ensuring high efficiency. In particular, compared with other interpolation-based algorithms, SF-FSA only needs the fast Fourier transform (FFT) and complex multiplication, which is easier to implement on FFT-based signal processors and fast parallel signal processors. By the formula derivation of SF-FSA, the problem on how to select parameters in the frequency scaling operation in SF-FSA is solved, the aliasing phenomenon caused by the frequency scaling operation in SF-FSA is explained, and the corresponding method to avoid the aliasing phenomenon is given. Finally, the validity and feasibility of SF-FSA are demonstrated by simulation and experimental results.

Two-Dimensional Imaging of FMCW Ice Sounding Radar Data via the Modified Frequency Scaling Algorithm

In this paper, we propose a novel processing algorithm for FMCW ice-sounding radar which addresses the along-track focusing of internal reflecting horizons. It is a modified frequency scaling algorithm that can handle the two-layer medium imaging model as well as the Doppler impact of the motion within the sweep in a nadir-looking radar. The proposed algorithm’s comprehensive derivation and implementation processes are described. In the range-Doppler domain, a really effective formulation for radar signals is also presented, which takes into account refraction effects and electromagnetic wave propagation velocity changes at the interface of two different mediums. Point target simulations are carried out to demonstrate the algorithm’s performance. The proposed method is also tested on the real data acquired by the Shallow-Layers-Detection Ice Sounding Radar (SLDISR). The result shows the applicability of the proposed method for imaging the ice sheet.

Are task mappings with the highest frequency of servers so good? A case study on Heterogeneous Earliest Finish Time (HEFT) algorithm

In the current heterogeneous computing environment, scheduling strategies play a key role in achieving high performance under current heterogeneous computing environment. Various algorithms have been proposed to generate high-quality schedules. Nonetheless, all the existing methods initialize task mapping with the highest frequencies of servers, creating significant effects on the results. Does mapping with the highest frequencies of servers generate the best schedule? In this paper, an attempt has been made to answer this question by investigating HEFT, which is a commonly used initial schedule method in scheduling the literature under DVFS environments. In this work, the task mapping is initialized with different server frequency level, along with investigating its impacts on the schedule results. Based on this concept, a corresponding Frequency Scaling Algorithm (FSA) has been proposed. By applying FSA combined with three frequency levels mappings, it has been observed that initial task mapping with middle server frequencies can generate much better results. When the initial frequency is the highest, the scheduling result is not optimal. The proposed algorithm requires less execution time and generates stable performance. Also, the proposed frequency scaling method is found to mitigate the impacts of initial mappings to a certain extent.

Compressive Sensing SAR Imaging Algorithm for LFMCW Systems

Linear frequency-modulated (LFM) continuous-wave (CW) radar is usually the first choice in synthetic aperture radar (SAR) imaging missions due to its relatively low cost and hardware simplicity. However, the use of continuous-wave unnecessarily introduces the problem of intrapulse motion. Furthermore, a large amount of data generated by the CW imaging process may overburden the onboard communication system with its high streaming data rate. On the other hand, a large amount of data may, indeed, not be necessary for high-resolution SAR imaging. In this article, we took full consideration of the intrapulse motion in LFMCW radar systems and modeled the phase preserving extended frequency scaling algorithm (EFSA) reconstruction process with far fewer data samples as compressive sensing problem and used subgradient descent algorithm with optimal step size to realize compressive sensing reconstruction. Our compressive sensing problem is different from the traditional direct inversion-based problem in that our reconstructed results contain both mainlobe and sidelobes. Comparisons were made with mainstream iterative soft thresholding-based reconstruction algorithm to demonstrate its capability to reconstruct large imaging scenes with high resolution. Both simulations and experiments with measured data have verified our proposed algorithm.

Efficient Frequency Scaling Algorithm for Short-Range 3-D Holographic Imaging Based on a Scanning MIMO Array

Millimeter-wave (MMW) holographic imaging technology is widely used in plenty of short-range applications like security and medical diagnosis. When combining with multiple-input-multiple-output (MIMO) array, such a technology can acquire precise reconstruction with wider field of view and higher dynamic range. However, to focus the higher dimensional data set obtained from MIMO architecture, the complicated iteration or interpolation employed by the previous state-of-the-art focusing techniques prevents the real-time operation of such an imaging system under a general computation power. It is more economical to increase the operational speed by improving the algorithm efficiency. Hence, a novel fast imaging algorithm that uses multistatic frequency scaling technique is proposed in this article for achieving real-time 3-D imaging on a 1-D MIMO scanning system. Only fast Fourier transform (FFT)/inverse FFT (IFFT) and multiplications are employed in the algorithm, which can be easily implemented. Compared with the previous state-of-the-art techniques, the proposed algorithm has the lower computation complexity. Practical experiments with self-developed MMW MIMO scanning radar prove the accuracy and efficiency of the algorithm. On a common laptop without any acceleration technology, the proposed algorithm requires less than one tenth of the time as required by the previous state-of-the-art techniques.

Airborne FMCW SAR Sparse Data Processing via Frequency-Scaling Algorithm

Using the continuous-wave technology to replace the conventional pulse-mode, frequency-modulation continuous-wave (FMCW) synthetic aperture radar (SAR) has shown good potentials of reducing the weight of the system and the sensors' peak transmission power. In order to relax the requirements of data bandwidth and storage, and increase the swath, the SAR system will collect the downsampled data, which makes the traditional matched filtering (MF)-based method unable to recover the considered scene, leading to failed reconstruction. To solve this problem, this letter presents an FMCW SAR sparse imaging method based on the frequency-scaling algorithm (FSA). Experimental results on the real data show that compared with the MF-based FMCW SAR imaging algorithms, the proposed method can improve the recovered image performance effectively. For the sparse surveillance region, it can achieve accurate recovery even from the downsampled data. Because the computational complexity of the proposed method is in the same order as that of MF, the sparse imaging of large-scale scenes can also be realized in the FMCW SAR.

An Efficient Imaging Algorithm for GNSS-R Bi-Static SAR

Global Navigation Satellite System Reflectometry (GNSS-R) based Bi-static Synthetic Aperture Radar (BSAR) is becoming more and more important in remote sensing, given its low power, low mass, low cost, and real-time global coverage capability. Due to its complex configuration, the imaging for GNSS-R BSAR is usually based on the Back-Projection Algorithm (BPA), which is very time consuming. In this paper, an efficient and general imaging algorithm for GNSS-R BSAR is presented. A Two Step Range Cell Migration (TSRCM) correction is firstly applied. The first step roughly compensates the RCM and Doppler phase caused by the motion of the transmitter, which simplifies the SAR data into the quasi-mono-static case. The second step removes the residual RCM caused by the motion of the receiver using the modified frequency scaling algorithm. Then, a cubic phase perturbation operation is introduced to equalize the Doppler frequency modulation rate along the same range cell. Finally, azimuth phase compensation and geometric correction are completed to obtain the focused SAR image. A simulation and experiment are conducted to demonstrate the feasibility of the proposed algorithm, showing that the proposed algorithm is more efficient than the BPA, without causing significant degradation in imaging quality.

An energy-efficient time-triggered scheduling algorithm for mixed-criticality systems

Real-time safety-critical systems are getting more complicated due to the introduction of mixed-criticality systems. The increasing use of mixed-criticality systems has motivated the real-time systems research community to investigate various non-functional aspects of these systems. Energy consumption minimization is one such aspect which is just beginning to be explored. In this paper, we propose a time-triggered dynamic voltage and frequency scaling (DVFS) algorithm for uniprocessor mixed-criticality systems. We show that our algorithm outperforms the predominant existing algorithm which uses DVFS for mixed-criticality systems with respect to minimization of energy consumption. In addition, ours is the first energy-efficient time-triggered algorithm for mixed-criticality systems. We prove an optimality result for the proposed algorithm with respect to energy consumption. Then we extend our algorithm for tasks with dependency constraints.

Energy-Aware CPU Frequency Scaling for Mobile Video Streaming

The energy consumed by video streaming includes the energy consumed for data transmission and CPU processing, which are both affected by the CPU frequency. High CPU frequency can reduce the data transmission time but it consumes more CPU energy. Low CPU frequency reduces the CPU energy but increases the data transmission time and then increases the energy consumption. In this paper, we aim to reduce the total energy of mobile video streaming by adaptively adjusting the CPU frequency. Based on real measurement results, we model the effects of CPU frequency on TCP throughput and system power. Based on these models, we propose an Energy-aware CPU Frequency Scaling (EFS) algorithm which selects the CPU frequency that can achieve a balance between saving the data transmission energy and CPU energy. Since the downloading schedule of existing video streaming apps is not optimized in terms of energy, we also propose a method to determine when and how much data to download. Through trace-driven simulations and real measurement, we demonstrate that the EFS algorithm can reduce 30 percent of energy for the Youtube app, and the combination of our download method and EFS algorithm can save 50 percent of energy than the default Youtube app.

Real-Time Control for Power Cost Efficient Deep Learning Processing With Renewable Generation

The explosive increase in deep learning (DL) deployment has led GPU power usage to become a major factor in operational cost of modern HPC clusters. The complex mixture of DL processing, fluctuated renewable generation, and dynamic electricity price impedes the elaborate GPU power control, so as to lead an undesirable cost. However, most previous studies have been concerned only with the design of power management method using DL, and have not care about the cost caused by GPU power consumption for DL processing itself. This paper, as the opposite direction of these trends, proposes a real-time power controller called DeepPow-CTR for cost efficient DL processing in GPU based clusters. We design the GPU frequency scaling algorithm based on model predictive control (MPC), to delicately tune the DL power consumption in response to dynamic renewable generation and electricity price. At the same time, we avoid the unacceptable DL performance degradation by regulating memory-access / feed-forward / back-propagation (MFB) time per each minibatch data in deep neural network (DNN) model training. To solve the designed nonlinear MPC problem rapidly and accurately, we apply the damped Broyden-Fletcher-Goldfarb-Shanno (BFGS) based sequential quadratic programming (SQP) method to our DeepPow-CTR. Our experimental results on lab-scale testbed using real trace data of renewable generation and electricity price, demonstrate that the proposed DeepPow-CTR has superiority and practicality in terms of DL processing power cost and performance, compared to existing methods.

Energy conscious multi-site computation offloading for mobile cloud computing

In mobile devices, one of the major reasons for battery consumption is due to computation of complex applications. To provide high performance of execution on the mobile devices, the concept of mobile cloud computing (MCC) is used. MCC allows the computation complex modules to offload onto a cloud from a mobile device, which helps to remove the resource constraint condition of the mobile devices. Computations can also be offloaded to nearby clouds called multi-sites, which may have different resources, access delays, computation capability or service charges. The mobile users can specify their priority such as total completion time, cost or energy saving of the application execution in MCC environment. But most of the existing research is focused to optimize only one objective, i.e., either total completion time or cost or energy. But when offloading computation modules onto cloud-based multi-sites, a tradeoff solution is required to strike a balance between the total completion time and energy savings. Further, the entire computational execution on the cloud is to be served efficiently with optimal power utilization. Various algorithms are developed to reduce power consumption, and one such algorithm is dynamic voltage and frequency scaling (DVFS) algorithm. In this paper, new algorithms known as cost and time constraint task partitioning and offloading algorithm (CTTPO), multi-site task scheduling algorithm (MTS) based on teaching, learning-based optimization and the energy saving on multi-sites (ESM) using DVS technique are proposed. CTTPO deals with trade-off between time and cost for the task partitioning and offloading. The MTS algorithm deals with time efficient scheduling on multi-sites, and ESM algorithm saves the energy on the multi-sites by switching the sites from high voltage to low voltage during ideal time. The simulation study demonstrates that the proposed algorithms outperformed the existing techniques based on time, cost and energy parameters.

Highly squinted SAR imaging simulation of ship‐ocean scene based on EM scattering mechanism

To investigate highly squinted spotlight synthetic aperture radar (SAR) images of an electrically large ship target over a rough sea surface, this work focuses on the simulation analysis of SAR images from such a composite scene. For this problem, there are two key issues need to be considered, namely the simulation and the processing of SAR echoes. Considering the first issue, an efficient facet scattering model based on capillary wave modification facet scattering model and geometrical optics and physical optics hybrid method is applied to calculate the electromagnetic (EM) scattering characteristics from a real ship-ocean scene, based on which SAR echoes can be obtained. For the second issue, a non-linear frequency scaling algorithm (NFSA) is employed to efficiently process the highly squinted SAR echoes. Compared with the traditional frequency scaling algorithm, the NFSA extends the frequency scaling operation to the cubic order and makes a more accurate secondary range compression. With the solutions to the two issues, SAR images of a complicated ship-ocean scene under different incident and squint angles are presented and analysed. The reasonable results demonstrate the validity of the simulation approach and the practicability of the model for highly squinted spotlight SAR images.

Numerical simulation of highly squinted spotlight SAR images from complex targets using an amendatory frequency scaling algorithm

ABSTRACTIn the highly squinted spotlight synthetic aperture radar (SAR) image simulation of complex electrically large targets, the principal difficulties consist of the generation and the processing of SAR echo. Concerning the above two issues, this paper proposed a complete procedure for highly squinted SAR image simulation. Firstly, in order to generate the SAR echo efficiently and accurately, the geometrical optics and physical optics (GO-PO) hybrid method is employed to get the spatial distributed scattering fields, which is an essential step for the generation of SAR echo. Secondly, regarding to the processing of highly squinted SAR echo, the amendatory frequency scaling algorithm (AFSA) is adopted. Compared with the benchmark frequency scaling algorithm (FSA), AFSA eliminates the trouble caused by the range-dependent secondary range compression (SRC) error in high squint mode through the nonlinear frequency scaling (NFS) operation. Finally, according to the complete simulation procedure, SAR images of a complex ship target under different radar parameters are simulated and analysed. The simulation results indicate the performance of AFSA in the processing of highly squinted SAR echo. Meanwhile, the effectiveness and the reasonability of the complete simulation procedure are revealed as well.

Wide-scene airborne ladar imaging exploiting the synthetic aperture technique with terrain observation by progressive scans

The image scene is limited by the small size of the spot, which is linked with the short wavelength of the laser in synthetic aperture ladar (SAL). The common wide-scene imaging mode in synthetic aperture radar, i.e., the terrain observation by progressive scans imaging mode, is extended and applied in SAL. A suitable imaging algorithm is also proposed according to the characteristics of the signal. First, the azimuth prefiltering based on the spectrum analysis is adopted to eliminate the azimuth spectrum aliasing caused by the steering of the antenna beam. Then, the improved frequency scaling algorithm is applied to complete the range compression and range cell migration correction. Meanwhile, the Doppler frequency shift induced by the continuous moving of the platform is compensated. Finally, the azimuth compression is completed in the Doppler domain, and the SAL image without ambiguity is obtained with the application of the dechirp technique. Simulation results provide the evidence on the effectiveness of the proposed method.

Study on Full Aperture Imaging Algorithm for Airborne TOPS Mode

The beam-scan allows the terrain observation by progressive scans (TOPS) mode to cover large azimuthal scenes in a short time, which also causes aliasing in both Doppler domain and azimuth time domain. In the TOPS mode, the most popular solution to overcome the aliasing problem is the “sub-aperture” method, which introduces sub-aperture partition and stitching operations. Aiming at an airborne digital array frequency modulated continuous wave (FMCW) SAR system, this paper discusses the removal of azimuth aliasing in the TOPS mode based on a full aperture imaging structure, by analyzing the Doppler characteristics of the echo signal of the airborne TOPS mode, the aliasing problem in the Doppler domain is avoided by properly setting system working parameters, which simplifies the imaging procedure; in addition, drawing up the idea of the de-rotation operation, we propose an improved frequency scaling algorithm, which removes the aliasing in the azimuth time domain without increasing the amount of processing data, and the algorithm contains only fast Fourier transformation and complex multiplication operations. The simulation results show that the algorithm can achieve the well-focused images of the airborne TOPS mode even in the case of serious aliasing in the azimuth time domain.