Real-Time Implementation of Chirp Scaling Algorithm

At the present time, the strip mode aircraft Synthetic Aperture Radar (SAR) processing techniques are relatively more mature than the spaceborne SAR processing techniques. In airborne SAR, the exact formulation for the point target response has been made in both the time and frequency domain. Efficient and accurate wave domain processing techniques such as the chirp scaling algorithm, have thus far been optimized for airborne SAR systems. With regards to spaceborne SAR processing, the existing analysis and processing algorithms are only applicable to narrow beam systems where the phase function is well approximated by the second order polynomial. There were also no detailed studies to show the applicability and the performance of the chirp scaling algorithm when applied to the spaceborne SAR systems. This paper provides a proposed solution and the supporting analysis for extending spaceborne SAR processing techniques to cases having wide beam angles as well as large squint angles. Detailed discussion are made on how to adapt the chirp scaling algorithm to spaceborne SAR, the requirement in updating processing parameters, and the approach for flexible pixel location and higher phase fidelity.

The availability of synthetic aperture radar (SAR) data offers great potential for environmental monitoring due to the insensitiveness of SAR imagery to atmospheric and sun- light-illumination conditions. In addition, the short revisit time provided by future SAR-based missions will allow a huge amount of multitemporal SAR data to become systematically available for monitoring applications. In this paper, the problem of detecting the changes that occurred on the ground by analyzing SAR im- agery is addressed by a completely unsupervised approach, i.e., by developing an automatic thresholding technique. The image- ratioing approach to SAR change detection is adopted, and the Kittler and Illingworth minimum-error thresholding algorithm is generalized to take into account the non-Gaussian distribution of the amplitude values of SAR images. In particular, a SAR-specific parametric modeling approach for the ratio image is proposed and integrated into the thresholding process. Experimental results, which confirm the accuracy of the method for real X-band SAR and spaceborne imaging radar C-band images, are presented.

Traditional Synthetic Aperture Radar (SAR) imaging is the projection of a real three-dimensional scene onto a two-dimensional domain of azimuth and slant range, which results in the loss of the high-dimensional information. With the advancement of SAR system and its processing technology, tomographic SAR systems obtain multiple data along the height direction to construct the high-dimensional synthetic aperture, and use array signal processing methods to achieve high-resolution three-dimensional images. It can reconstruct the observation scene and extract vertical structure information of the ground target, which is very important for vegetation monitoring, snow and ice detecting, and urban modeling. This paper analyzed the key steps of three-dimensional imaging, such as image registration, flat-earth phase removal, phase compensation, and the three-dimensional focusing, as well as the current research status of each step based on the observation mechanism of tomographic SAR system. This paper particularly focuses on using tomographic SAR on the application of vegetation, glacier, snow, and urban information. The most relevant experimental results in the past two decades were introduced. Further, the application potential and existing problems related to the vegetation height with canopy structure, glacier thickness with internal structure, snow thickness with stratification, and urban three-dimensional reconstruction with deformation monitoring under different platforms are discussed. Finally, the prospects of TomoSAR in the primary applications field are presented.

<p>The large availability of Synthetic Aperture Radar (SAR) data collected over the last decade by several satellite missions, such as COSMO-SkyMed, TerraSAR-X and Sentinel-1 constellations, has been pushing toward the present Earth Observation (EO) scenario into a “golden age”, which is rapidly moving towards a real Big Data scenario. The widespread use of satellite SAR data have fostered the development of several SAR applications, one of those referred to as Differential SAR Interferometry (DInSAR) technology, which has deeply demonstrated to profitably detect the surface deformations over a wide spatial extent in both natural and anthropic hazard scenarios, through the generation of spatially dense deformation maps with millimetric accuracies. In particular, the advanced DInSAR algorithm referred to as Parallel Small BAseline Subset (P-SBAS) approach allows retrieving the temporal and spatial characteristics of the detected displacements at two spatial resolution scales, referred to as regional (spatial resolution in the 30-90 m range) and local (spatial resolution in the 3-10 m range) scales, suitable for a large variety of application fields, from natural hazards (volcano eruptions, seismic events, landslides) to anthropic contexts (urban areas, archaeological sites, oil-gas extraction, structures and transport infrastructures).</p><p>However, the interferometric processing performed at local scale needs to deal with hundreds of SAR acquisitions at full spatial resolution, i.e. to manage several hundreds of million points; consequently, the processing of such a data amount is particularly heavy from a computational point of view and can not be carried out in reasonable time frames through the traditional (sequential) implementation of the full resolution DInSAR processing chains.</p><p>Accordingly, to profitably benefit from the current SAR scenario, it is crucial to develop innovative solutions to automatically and efficiently handle large DInSAR data stacks. These solutions are principally based on the exploitation of advanced distributed computing environments, to achieve high efficiency in terms of scalability performances, as well as on the development of much more advanced DInSAR methodologies (and codes) able to effectively maximize the information related to these huge amount of DInSAR data.</p><p>This work is aimed at describing an innovative DInSAR solution, based on the exploitation of distributed HPC and Cloud Computing environments, which benefits from parallel programming techniques (multi-node, multi-threads, GPUs) implemented within an automatic full resolution P-SBAS processing pipeline. Starting from large SAR datasets acquired by the COSMO-SkyMed constellation, the developed parallel full resolution P-SBAS processing chain allows retrieving in short time frame (less than 24 hours) displacement time series and deformation maps, at the single buildings/infrastructure level, relevant to extended urban areas. The presented experimental results and the related performance analyses are achieved by applying the developed parallel P-SBAS pipeline to a number of large full resolution COSMO-SkyMed datasets acquired over some important Italian cities (e.g. Rome and Naples urban areas).</p>

A new methodology to process received signal in X band ultra-high resolution synthetic aperture radar (SAR) is presented in this paper. The received wideband signal is decomposed into sub bandwidth signals firstly, which are compressed by the chirp scaling algorithm (CSA) and then combined in 2-D frequency domain to obtain the final image. Compared with original CSA, this bandwidth segmentation method improves SAR image quality and extends the focused scene size effectively. Point target simulation validates the algorithm. Index Terms—ultra-high resolution SAR; bandwidth segmentation method; chirp scaling algorithm; 2-D frequency domain combination

For a synthetic aperture radar (SAR) onboard a platform with a rectilinear track, the range history of a point target can be accurately expressed hyperbolically. The track can be curvilinear for a maneuverable SAR platform. The hyperbolic equation becomes inadequate, and an expression with high-order terms is needed. Using the method of series reversion, we derived the 2-D spectrum for the return signal of the curvilinear SAR. There were five independent variables in the spectrum, but available imaging algorithms could only handle three in the focusing using the spectrum. Thus, a variable-decoupling method was developed to reparameterize the initial spectrum so that only three variables remained. After the incorporation of the decoupling method into the chirp-scaling algorithm, simulations of the SAR with a curvilinear track were studied. Promising results were obtained.

The algorithms that processes electromagnetic back scattered echo holds importance in Synthetic Aperture Radar (SAR) imaging. The selection criteria of an appropriate algorithm depend on application requirement of SAR technique. The computational complexity of Range Doppler Algorithm (RDA) and Chirp Scaling Algorithm (CSA) differs in terms of the procedure adopted for Range Cell Migration Correction (RCMC). In this paper, computational complexities and structural similarities between processed images by these two algorithms are studied through their processing steps on varying pixel size images with the help of MATLAB tool.

An extended nonlinear chirp scaling (NLCS) algorithm is proposed to process data of highly squinted, high-resolution, missile-borne synthetic aperture radar (SAR) diving with a constant acceleration. Due to the complex diving movement, the traditional signal model and focusing algorithm are no longer suited for missile-borne SAR signal processing. Therefore, an accurate range equation is presented, named as the equivalent hyperbolic range model (EHRM), which is more accurate and concise compared with the conventional fourth-order polynomial range equation. Based on the EHRM, a two-dimensional point target reference spectrum is derived, and an extended NLCS algorithm for missile-borne SAR image formation is developed. In the algorithm, a linear range walk correction is used to significantly remove the range-azimuth cross coupling, and an azimuth NLCS processing is adopted to solve the azimuth space variant focusing problem. Moreover, the operations of the proposed algorithm are carried out without any interpolation, thus having small computational loads. Finally, the simulation results and real-data processing results validate the proposed focusing algorithm.

Digital beamforming (DBF) synthetic aperture radar (SAR) on receive in elevation is a promising candidate to realize the demanding requirement of high resolution and wide swath (HRWS) in remote sensing. But this technique suffers from a degraded imaging performance due to the incoherence between the received signals from different subchannels. When SAR sensor transmits a long pulse, the output of DBF network will be seriously modulated within recorded time window. The amplitude modulation of beamformer output will introduce severe bandwidth loss, thus resulting in a decreased resolution and imaging magnitude. In order to overcome the deficiency mentioned above, this paper puts forward an improved phase compensation method for range DBF SAR system. Through phase corrections of the received signals from all the subarrays, the echoes of multiple channels can be coherently synthesized. Consequently, the proposed method can eliminate the amplitude modulation effect of beamformer, and subsequent imaging quality can be significantly improved. Both numerical analysis and experimental results testified the favourable improvements of this proposed strategy.

Nowadays, due to the need of high resolution imaging, huge amount of data are needed to be collected base on Nyquist sampling theorem. However, the acquisition platform cannot afford the computation requirement to process on board, so those data must be sent to the ground so that it can be processed. This paper is focused on the compression of the Synthetic Aperture Radar (SAR) raw data to send as less data as we can ease the burden on the system and reduce the time for transmission. In this paper, we compressed the SAR raw data using compressive sensing method, and we train the sparse basis through K-SVD method. First we use the raw data that we collected to train the sparse basis using K-SVD method, when we get the trained sparse basis, we only need to send part of the raw data with the basis to the ground and the raw data can be recovered perfectly. The result of recovered data and imaging results are given. This method can help us to perform further application research of SAR imaging.

Raw signal simulation is a useful tool for synthetic aperture radar (SAR) system design, mission planning, processing algorithm testing, and inversion algorithm design. Time and frequency synchronization is the key technique of bistatic SAR (BiSAR) system, and raw data simulation is an effective tool for verifying the time and frequency synchronization techniques. According to the two-dimensional (2-D) frequency spectrum of fixed-receiver BiSAR, a rapid raw data simulation approach with time and frequency synchronization errors is proposed in this paper. Through 2-D inverse Stolt transform in 2-D frequency domain and phase compensation in range-Doppler frequency domain, this method can significantly improve the efficiency of scene raw data simulation. Simulation results of point targets and extended scene are presented to validate the feasibility and efficiency of the proposed simulation approach.

A Multi-Input Multi-Output (MIMO) Synthetic Aperture Radar (SAR) system has been considered for its potential to simultaneously improve the performance of stationary scene imaging and ground moving target indication (GMTI) by utilizing multiple subarrays both at transmission and reception. To successfully exploit the potential benefits of MIMO SAR for practical imaging application, a signal model for MIMO SAR system with colocated arrays is presented in this paper, then we propose a novel procedure for imaging processing by coherently combining the returns from multiple receive subarrays. Our approach can make efficient use of the transmitted waveform power, and resulting in higher Signal-to-Noise Ratio (SNR) images. The effectiveness of model and MIMO SAR imaging method proposed in this paper are demonstrated by simulation experiments.

The paper introduces an algorithm for Ultrawideband Ultrawidebeam (UWB) bistatic Synthetic Aperture Radar (SAR). The algorithm works in time-domain and therefore inherits time-domain characteristics such as unlimited scene size, local processing and manageable motion compensation. The proposed algorithm is not limited by any configuration of bistatic SAR. The algorithm processes the UWB bistatic SAR data on a subaperture and subimage basis. This means, instead of backprojecting directly the SAR data to a ground image plane, the algorithm handles the data in two stages: beam forming and local backprojection. The algorithm is named Bistatic Fast Backprojection (BiFBP) and has been tested successfully with the simulated UWB bistatic SAR data.

The fractional Fourier transform (FrFT), which is a generalized form of the well-known Fourier transform, has opened up the possibility of a new range of potentially promising and useful applications including radar involving the use and detection of chirp signals, pattern recognition and Synthetic Aperture Radar (SAR) image processing. The Chirp Scaling Algorithm (CSA) is one of the most important and well-known radar imaging algorithms. It is attractive because of its excellent focusing ability and implementation simplicity. Benefiting from the inherent structure of the FrFT for non- stationary digital signal processing and analysis, especially for chirped-type signals, a new version of the CSA based on the Fractional Fourier Transform (FrFT) is developed. The introduced Fractional Chirp Scaling Algorithm (FrCSA) applied the Fast Fourier Transform (FFT) instead of the fractional Fourier transform (FrFT) in the azimuth direction for the analytical development tractability purposes only as it numerically tractable in both dimensions. To demonstrate the resolution and focusing enhancement in the azimuth dimension using the FrCSA and also to be able to perform azimuth fractional filtering, noise removal and flight path nonlinearity compensation, a closed form expression for the azimuth fractional transformation is required. In this talk we present the mathematical derivation for a closed- form expression of the azimuth-fractional Fourier transform of the new FrCSA with application to high resolution imaging. Results to real SAR data images will show significantly enhanced features using the FrFT-based azimuth expression instead of the classical FFT-based one within the fractional chirp scaling algorithm or any other chirped-type SAR imaging algorithm.

With appropriate geometry configurations, bistatic Synthetic Aperture Radar (SAR) can break through the limitations of monostatic SAR on forward-looking imaging. Thanks to such a capability, bistatic forward-looking SAR (BFSAR) has extensive potential applications. However, the current BFSAR imaging algorithms always involve the time-consuming interpolation to carry out range migration correction. This paper develops a Chirp Scaling type imaging algorithm, which just needs the complex multiplication and FFT, for BFSAR with two parallel moving stations. The simulation results validate the effectiveness of the method.