digital signal processing of synthetic aperture radar data algorithms and implementation pdf

Digital Signal Processing Of Synthetic Aperture Radar Data Algorithms And Implementation Pdf

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This special issue arises from the spread of low-cost radar sensors and processing units which offer an extended range of applications.

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Digital Processing of Synthetic Aperture Radar Data: Algorithms and Implementation

In the signal processing software testing for synthetic aperture radar SAR , the verification for algorithms is professional and has a very high proportion. However, existing methods can only perform a degree of validation for algorithms, exerting an adverse effect on the effectiveness of the software testing. This paper proposes a procedure-based approach for algorithm validation.

Firstly, it describes the processing procedures of polar format algorithm PFA under the motion-error circumstance, based on which it analyzes the possible questions that may exist in the actual situation. By data simulation, the SAR echoes are generated flexibly and efficiently.

Then, algorithm simulation is utilized to focus on the demonstrations for the approximations adopted in the algorithm. Combined with real data processing, the bugs concealed are excavated further, implementing a comprehensive validation for PFA.

Simulated experiments and real data processing validate the correctness and effectiveness of the proposed algorithm. Synthetic aperture radar SAR [ 1 , 2 ] systems achieve high spatial resolutions by transmitting wide bandwidth linear frequency modulation LFM signal combined with long azimuth illumination time, which have been widely applied in both military and civilian areas.

As digital technologies are continuously developing into maturity, the proportion of software within SAR systems is growing, so is that of functions implemented by software. Particularly, a large number of algorithms are necessary for SAR signal processing, such as beam forming and impulse compression. These signal processing algorithms account for a large proportion and are professional, which makes it difficult to find out the problems hidden.

As a foundation for radar detection, the function and performance of SAR would be severely affected once the signal processing algorithms failed. To guarantee the quality [ 3 — 5 ] of SAR systems, it is indispensable to assure the quality of software in them. Hence, the validation for imaging algorithms is an important way to assure the software quality, which becomes a critical segment for SAR software testing [ 6 — 9 ].

In essence, the algorithm validation for SAR signal processing software mainly focuses on the adopted hypotheses together with approximations, the questions that may encounter in algorithm implementation, the processing performances under the circumstances of different parameters, and various external conditions. During the imaging algorithms, approximations can simplify the signal processing; however, they can simultaneously exert negative influences on the effective imaging scope and the final imaging results.

In most cases, these influences are difficult to be accurately expressed by mathematical formula; instead, they need many trials to dig out the bugs deep inside the imaging algorithms.

Moreover, SAR echoes are corrupted by noises, clusters, and interferences under a certain condition like the complicated combat environment, which imposes higher requirements for the adaptability and robustness for related processing algorithms. Now, the algorithm validation for SAR signal processing software primarily utilizes the black box testing method: checking the collected SAR echoes with the help of simulators or real equipment and observing the imaging results displayed on the interface so as to perform an indirect verification for the correctness of the algorithm implementation.

However, this method can achieve a partial validation for the algorithms to some extent, dramatically decreasing the effectiveness of signal processing software testing. In this paper, the validation of the imaging algorithm is organized as follows. The SAR echo data model and the procedure of polar format algorithm PFA are presented, figuring out the hypotheses and approximations that should be paid attention to in software testing.

The improved concentric circle method ICCM is adopted to flexibly configure the system parameters and to quickly generate the SAR echoes needed. Then, the simulation experiments are designed to verify the correctness of PFA as well as its implementation in both motion-error-free and motion-error conditions, severally. Besides, considering the influences of external environment, real data processing is performed as a validation for real scenario to further inspect the performance, which can effectively complement the shortcomings of the former operations from a different perspective.

Finally, simulation experiments and real data processing are employed to demonstrate the correctness and effectiveness of the proposed method. There are two kinds of SAR imaging algorithms: frequency-domain imaging algorithm and time-domain imaging algorithm.

Frequency-domain imaging algorithm assumes that the SAR platform travels a perfect linear uniform acquisition trajectory.

Approximations are usually made for dramatically decreasing the computational burden with slight precision degradation. Different frequency-domain imaging algorithms almost have an identical processing idea but vary at the way of approximations for range-azimuth decoupling.

Widely applied in high-resolution spotlight SAR, the polar format algorithm PFA is a typical frequency-domain imaging algorithm and has the ability to accurately combine with autofocusing algorithms [ 10 — 12 ] to efficiently remove the residual phase errors. Figure 1 describes the geometry of SAR echo data collection, where the antenna phase center APC moves along the X -axis azimuth direction with a constant velocity at , forming an ideal trajectory as the dotted line illustrated.

The radar beam operates in the squint angle , and the instant corresponding to , the center of the synthetic aperture, is at slow time. Construct a two-dimensional coordinate with the origin. The coordinates of the scene center point target and an arbitrary target are and , respectively, where is the reference range.

Due to the presence of atmospheric turbulences and carrier perturbations, SAR platform travels a curve trajectory and the instantaneous slant range from the APC to P can be expressed as where is the nominal instantaneous range between the APC and P , signifies the instantaneous azimuth position of APC, and represents the slant error.

Suppose radar transmits LFM signal, the received baseband radar echo can be given by where denotes the rectangular window function, is the azimuth window function, is the impulse width, depicts the fast time, means the chirp rate, defines the center frequency of the carrier, indicates the double time delay from the APC to , and is the transmitted speed of the light in vacuum. The flowchart of PFA processing is illustrated in Figure 2.

In ideal case, range compression is firstly performed for the SAR echo data followed by azimuth dechirp. It should be noted that the sampling rate in the range dimension changes after the range interpolation. Accordingly, the interval between the adjacent pixels in the range dimension changes and so does the range scope of the image. In the same way, the azimuth width of the image alters. Therefore, the scope of the final image should be paid attention to. In addition, the final imaged positions of the point targets have some certain deviations due to the limited validity of the underlying far-field approximation.

The distortion manifests in geometric shifts as well as in target defocusing and intensifies as distances between pixels and the scene reference position increase.

Actually, in order to eliminate these errors to an acceptable level, motion compensation MOCO must be included. By recording the relevant motion parameters with an onboard GPS or inertial measurement unit IMU system, the real movement of the SAR platform can be taken into account to carry out coarse compensation, i.

Nevertheless, the range interpolation and the azimuth interpolation change the forms of the remaining motion errors, resulting in an extra envelop deviation, i. All these factors should be considered when validating PFA. In addition, PFA focuses at frequency domain in the azimuth with length being greater than that of the aperture during the data collection, which indicates PFA can be used in subaperture signal processing.

The procedure-based approach, which is based on a detailed analysis on flowchart for PFA, implements a comprehensive verification for SAR signal processing software from three aspects: echo data simulation, validation via algorithm simulation and verification by real data processing. To achieve a rigorous validation for SAR imaging algorithms, echo data simulation is usually needed to efficiently generate the SAR echoes.

In this paper, the improved concentric circle method ICCM , which can flexibly configure the system parameters, is utilized to simulate point target echoes and distributed scene target echoes with a high accuracy and efficiency. Validation via algorithm simulation encompasses point target simulation and distributed scene target simulation. Combined with the flowchart of PFA, validation via algorithm simulation not only focuses on the effective scope of the imagery as well as the final focusing results of the point targets under ideal circumstances but also emphasizes on the approximations along with accuracies of motion compensation due to unpredicted platform motion or other propagation delays.

Thereinto, point target simulation can detect the problems hidden in the algorithm implementation promptly and reflect the probable defects of the algorithm accurately, which play a significant role in verifying SAR imaging algorithms. Due to the planar wave-front approximation [ 1 ], the focusing positions of point targets present certain fluctuations to some extent after strictly circling counterclockwise, inducing distortions of the image.

With the increasing range to the scene center, the focusing results of the associated targets become worse. PSLR refers to the proportion of the energy of first side lobe to that of the main lobe, which is denoted in decibel dB by. ISLR is the proportion of the energy of all side lobes to that of the main lobe, which yields where depicts the IRF of a certain point target. Besides, the intervals between two adjacent pixels change after the interpolation operations.

Correspondingly, the scope of the imaging area should be recalculated. For the distributed scene targets, the whole performance for the imagery can be measured by entropy and contrast. While entropy of the image indicates its gray-level distribution, contrast of the image tends to concentrate energy that has been blurred out by a phase distortion, which can be defined by where is the signal value of the row and the column of a focused image, and are the number of the range bins and that of the azimuth bins.

Generally, the smaller the entropy, the better the imaging results; the greater the contrast, the better the focusing effects. Validation via algorithm simulation under motion-error condition mainly includes the verifications for motion errors after two-dimension interpolation, the accuracies for NsRCM correction, and phase compensation.

The two-dimensional interpolation operation changes the forms of motion errors, resulting in an alteration to the envelop of the phase-history-domain signal. That is, significant defocusing exists if the NsRCM were not properly cancelled. To lower overall computational burden, approximations are always made for NsRCM and their accuracies can be assessed from two aspects: 1 observing whether the envelop of the phase-history-domain signal after NsRCM correction is restricted in a single range bin; 2 evaluating the final focusing performance via some representative indicators, such as PSLR, ISLR, and IRW.

Actually, phase errors have diverse forms for the targets that are located in different positions, i. When a uniform compensation is carried out, the residual phase errors may have an undesirable effect on the focusing performance and should be taken into account. We can roughly estimate the value range of the residual phase errors after the coarse compensation according to the accuracy of the GPS or inertial navigation system INS.

Then, we choose targets that are far away from the scene center, compensate for their phase errors with uniform and spatially variant corrections, respectively. Herein, spatially variant compensation can be implemented by dividing the phase-history-domain data into even blocks in the range dimension and correcting the corresponding phase errors one by one. Finally, we can draw a conclusion from the whole focusing performance of the final image as well as local focusing results of strong scatters.

In real SAR echo data, the true motion errors are unknown and the instantaneous positions of the SAR platform can only be obtained with the help of the measuring equipment.

Compared with simulation data, real data are collected in real scenario and usually corrupted by unknown clutters, uncertain noises, and different interferences. Hence, real data processing can be regarded as the validation of imaging algorithm in actual situation, which plays an important role that point target simulation and the distributed scene target simulation cannot replace.

The results of real data processing not only are good indicators for the performance and robustness of the imaging algorithm but also become a critical complement to algorithm simulation. Only when bugs do not exist in the algorithm simulation can real processing be conducted. Combined with corresponding autofocusing algorithms [ 10 , 11 ], such as weighted phase gradient autofocus WPGA , the motion errors can be corrected further.

In general, the effects for real data processing are directly related to the accuracy of the motion compensation. The more accurate the motion compensation is, the better the focusing performance becomes. While entropy and contrast can be utilized to judge the whole performance for the final results of the real data processing, specific corner reflectors are chosen to validate the focusing effects of strong scatters within the image. The two corner reflectors next to each other and distributed in a single range bin are usually extracted and interpolated to clearly reflect whether their main lobes of IRFs can be separated and to determine the azimuth resolution of the final image.

Due to the existences of the clusters, noises, and interferences, sharpening window functions [ 1 ] are usually adopted in range compression and azimuth compression to constrain the side lobes. In this part, echo data simulation, algorithm simulation experiments, and real data processing experiments are developed, focusing on the bugs that are easy to exist in SAR signal processing software, digging out the bugs concealed in the imaging algorithm from different perspectives and validating the effectiveness of the proposed method.

The SAR platform travels with a constant velocity along the right direction of - axis at the height of , forming a synthetic aperture , as presented in Figure 4. Simulation parameters are shown in Table 1. The sampling number in the range direction is and that in the azimuth direction is The elapsed time by the two methods is depicted in Figure 5 , where the horizontal axis represents base logarithmic for the number of the point targets and the vertical coordinate signifies the corresponding time consumed.

Algorithm simulation experiments include verification via PFA simulation under ideal circumstance, validation for the effects of motion error, and verification of distributed scene targets.

In this simulation experiment, spotlight SAR works in the squinted mode and system parameters are presented in Table 2. The theoretical values of the range resolution and the azimuth resolution are 0. Figure 6 b gives the imaging result after PFA processing. Accordingly, the analyzes on the results of the scene center point target O together with other point targets, like P and Q , are illustrated in Figures 7 a — 7 f.

In Figure 6 , the horizontal and vertical coordinates have some certain deviations from their theoretical positions except for the scene center point target O. Moreover, the focusing positions of P and Q are presented in Table 3 , from which we can see that the deviation increases with the range to O.

Digital Signal Processing Techniques and Applications in Radar Image Processing by

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. Cumming and F. Cumming , F. Wong Published Computer Science. Signal Processing Fundamentals. Pulse Compression.

[(Digital Signal Processing of Synthetic Aperture Radar Data: Algorithms and Implementation)] [Author: Ian G. Cumm. Book Download, PDF Download, Read.

Digital processing of synthetic aperture radar data : algorithms and implementation

Synthetic aperture radar SAR imaging is the most computational intensive algorithm and this makes its implementation challenging for real-time application. Fast Fourier transform FFT and multiplication operations with complex data types are the major units requiring heavy computation. The heuristic analysis of the algorithm using timing analysis and resource usage is presented. Very few literatures have presented the FPGA-based SAR imaging implementation, where analysis of windowing technique was a major interest. The timing analysis propagates that it is suitable to use this model for real-time SAR applications.

Advanced Techniques for Radar Signal Processing

Digital Processing of Synthetic Aperture Radar Data

This book is not available from inventory but can be printed at your request and delivered within weeks of receipt of order. This cutting-edge resource offers you complete how-to guidance on digital processing of synthetic aperture radar SAR data. You discover how SAR is used to obtain a high-resolution image from a satellite and learn the mathematical structure and spectral properties of the signal received from a SAR system. Supported with over equations and over figures, the book arms you with state-of-the-art signal processing algorithms and helps you choose the best algorithm for a given SAR system and image quality requirements. You also learn how to estimate the Doppler centroid frequency and azimuth FM rate from a geometry model or from received data. Written from a digital signal processing point of view, this authoritative volume can be fully understood by professionals with a general electrical engineering background. Home Login My Account.

However much of the Lung cancer Detection in matlab Recently, image processing techniques are widely used in several medical areas for image improvement in earlier detection and treatment stages, where the time factor is very important to discover the abnormality issues in target images, especially in various cancer tumours such as lung cancer, breast cancer, etc. Image quality and accuracy is the core factors of The capabilities range from basic SAR processing and visualization, to interferometry and time series analysis. Fewer options exist today for full featured cloud-native platforms that enable the average An up-to-date analysis of the SAR wavefront reconstruction signal theory and its digital implementation With the advent of fast computing and digital information processing techniques, synthetic aperture radar SAR technology has become both more powerful and more accurate. Processing of SAR Data shows how SAR imagery is formed, how interferometry SAR images are created, and gives you a detailed mathematical description of different focussing algorithms with special emphasis in interferomtery.

Foreword. Introduction. Signal Processing Fundamentals. Pulse Compression. Synthetic Aperture Concepts. SAR Signal Properties. The Range Doppler.

Synthetic-aperture radar SAR is a form of radar that is used to create two-dimensional images or three-dimensional reconstructions of objects, such as landscapes. SAR is typically mounted on a moving platform, such as an aircraft or spacecraft, and has its origins in an advanced form of side looking airborne radar SLAR. The distance the SAR device travels over a target in the time taken for the radar pulses to return to the antenna creates the large synthetic antenna aperture the size of the antenna. Typically, the larger the aperture, the higher the image resolution will be, regardless of whether the aperture is physical a large antenna or synthetic a moving antenna — this allows SAR to create high-resolution images with comparatively small physical antennas. Additionally, SAR has the property of having larger apertures for more distant objects, allowing consistent spatial resolution over a range of viewing distances.

In the signal processing software testing for synthetic aperture radar SAR , the verification for algorithms is professional and has a very high proportion. However, existing methods can only perform a degree of validation for algorithms, exerting an adverse effect on the effectiveness of the software testing. This paper proposes a procedure-based approach for algorithm validation. Firstly, it describes the processing procedures of polar format algorithm PFA under the motion-error circumstance, based on which it analyzes the possible questions that may exist in the actual situation. By data simulation, the SAR echoes are generated flexibly and efficiently.

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