An Improved Range-Doppler Algorithm for SAS Based on sparse Bayesian learning Two-Dimensional Deconvolution

For inverse synthetic aperture radar (ISAR) imaging of maneuvering targets, the Doppler frequency induced by an arbitrary scatterer on the target is time-varying, and hence, the traditional range-Doppler (RD) algorithm is not appropriate for obtaining focusing ISAR images. In such a scenario, a novel ISAR technique, called the range instantaneous Doppler derivative (RIDD) algorithm, has been recently proposed, and two different kinds of well-focused ISAR images, named range-Doppler centroid (RDC) frequency image and range-Doppler frequency rate (RDR) image, can be obtained. In order to improve the accuracy of the target classification and recognition, the scaling approach for the RIDD algorithm must be taken into consideration. In this article, the point spread function (PSF) representations and cross-range scaling factors of the RDC image and the RDR image obtained by the RIDD algorithm are demonstrated analytically. On the basis of the PSFs and cross-range scaling factors, a novel imaging and scaling approach for the RIDD algorithm in the presence of a low SNR environment based on smoothed integrated high-resolution time–frequency-rate representation (SIHR) combined with coherent integrated smoothed generalized cubic phase function (CISCF) is proposed. As the proposed algorithm has high estimation accuracy and good antinoise capability, precise parameter estimation of the received signal can be achieved. Hence, high-quality scaled RDC and RDR images of the target can be reconstructed by the proposed approach. Experimental results of simulated data and real measured data verify the correctness of the PSF representations and demonstrate the effectiveness of the imaging and scaling approach proposed in this article.

Software implementation of frequency-domain beamforming has become increasingly popular for fast imaging using ultrasound arrays. Stolt migration, which focuses aperture data in a 2-D frequency domain, has been adapted to ultrasound imaging, and can reduce beamforming time by up to two orders of magnitude compared to conventional delay-and-sum (DAS) beamforming. However, processing data in a 2-D frequency domain can require more memory to achieve sufficient accuracy and avoid interpolation errors. Here, we describe and apply the Range Doppler Algorithm (RDA) to focus ultrasound signals. RDA was first proposed for fast beamforming of monostatic data in radar remote sensing. RDA achieves image-reconstruction speed similar to Stolt migration, while all operations are done in one dimension at a time, allowing for efficient and intuitive implementation. Through simulation and phantom experiments, we show that RDA achieves similar image quality as DAS, while increasing the reconstruction speed by approximately a factor of 10.

Inverse synthetic aperture radar (ISAR) imaging has the competence of generating two-dimensional (2-D) high-quality images of remote moving targets, while interferometric ISAR (InISAR) is a technique to reproduce three-dimensional (3-D) high-resolution shapes of targets by employing several ISAR images obtained from different antennas. For the target moving with an invariable acceleration, the Doppler frequency shift of its echoes is proved to be time variant; consequently, the Range-Doppler (RD) algorithm will not work properly for acquiring the clear ISAR image of it. To produce high-resolution ISAR/InISAR image of the maneuvering target, a novel algorithm via the peak extraction technique is proposed. The main contributions made by this paper can be listed as follows: 1) by analyzing ISAR imaging with the RD technique for the target moving with a constant acceleration, it is elaborately demonstrated that each scattering point still corresponds to a unique peak in the defocused ISAR image; 2) the expression of the peak location in the defocused ISAR image corresponding to each scattering point is precisely derived; 3) a novel peak extraction technique is developed for forming a high-resolution radar image of the maneuvering target; 4) due to the discovery that the ISAR images obtained in this way can completely preserve the phase information, a new InISAR imaging method via the peak extraction technique is presented consequently. The correctness of the theoretical derivation and the performance of the new algorithms are illustrated through several simulation experiments.

Inverse synthetic aperture radar (ISAR) is an all-weather radar technique which may generate high-resolution images of noncooperative targets. The standard range-Doppler algorithm (RDA) is usually employed for image generation. However, the images obtained with RDA are usually blurred because of the relative motion between radar and target. As a consequence, motion compensation techniques should be used to improve the imagery quality. The range-instantaneous-Doppler (RID) technique based on time-frequency transforms has been proposed for obtaining a sequence of focused ISAR images without the need of using motion compensation techniques. However, in this letter, it is clearly shown that the migration of the target scatterers in slant range indirectly induces Doppler spreading of the scatterers' point spread function in each of the ISAR images obtained by the RID technique. This Doppler spreading means blurring. It is important to highlight that the migration in slant range may be caused not only by the radial component of the translational motion but also by the rotational motion. The application of motion compensation techniques prior to the use of the RID technique allows us to mitigate the Doppler spreading, as shown here both for simulated and live data acquired by a high-resolution coherent radar.

With two-order Taylor expansion formula and the equivalent transformation of the radical function, the double square rooted slant range history of multiple receiver synthetic aperture sonar (SAS) can be highly approximated. Based on that, system transfer function in two-dimensional frequency domain, which is required for fast imaging algorithms, is derived. Using the derived transfer functions, this paper presents a range-Doppler (R-D) algorithm for multiple receiver SAS based on our model. Then, simulated data is used to validate the presented method.

MIMO-SAR is a new radar imaging mode that has been proposed in recent years and has attracted much attention. It provides an effective way to solve the contradiction between high resolution and wide surveying bands faced by traditional synthetic aperture radars (SAR). In this paper, we overview the fundamental principle of MIMO-SAR imaging technology. Then, we establish a novel two-transmitter and four-receiver SAR imaging system based on linear frequency modulation (LFM) by combining multiple-input multiple-output (MIMO) technology with range Doppler (RD) algorithm to improve the imaging performance. Numerical simulation results are provided, which verifies that the MIMO-SAR imaging system has better imaging performance in terms of resolution and higher accuracy.

Bistatic inverse synthetic aperture radar (ISAR) can help image the high-speed target for its advantage in tracking and more observation angle to provide more information than monostatic ISAR. However, the complex image geometry makes it difficult to achieve a clear image of the target with geometry distortion correction and calibration. Furthermore, high-speed motion will make the image blurred or defocussed. To address these problems, a bistatic ISAR (B-ISAR) imaging method for high-speed motion target with geometric distortion correction and calibration is proposed. According to the motion decomposition idea, we established the B-ISAR echo model of the high-speed motion target. Then, based on the range Doppler algorithm, we deduce the analytic formula of the geometric distortion factor and calibration factor, and transform the imaging problem into a parameter estimation problem. With the sparsity of the scattering points, the required parameters are solved using the expectation maximization algorithm based on the maximum a posteriori probability criterion. With the estimated parameters, a clear B-ISAR image of a high-speed motion target with geometric correction and calibration is obtained. The simulations show that the proposed method has better resolution and simultaneously attains geometric distortion correction and calibration of the image.

Inverse Synthetic Aperture Radar (ISAR) is generally used for classification of ship targets after detection and tracking by the Radar. There are various methods for high resolution 2D imaging of maritime targets in ISAR mode like Range Doppler Algorithm and Polar Format Algorithm. After the raw image is formed, it has to pass through the post processing algorithm suite before an acceptable image can be displayed to observer. This paper presents an efficient algorithm suite for the post processing of ISAR image.

Guided image super-resolution (GISR) aims to obtain a high-resolution (HR) target image by enhancing the spatial resolution of a low-resolution (LR) target image under the guidance of a HR image. However, previous model-based methods mainly take the entire image as a whole, and assume the prior distribution between the HR target image and the HR guidance image, simply ignoring many non-local common characteristics between them. To alleviate this issue, we firstly propose a maximum a posteriori (MAP) estimation model for GISR with two types of priors on the HR target image, i.e., local implicit prior and global implicit prior. The local implicit prior aims to model the complex relationship between the HR target image and the HR guidance image from a local perspective, and the global implicit prior considers the non-local auto-regression property between the two images from a global perspective. Secondly, we design a novel alternating optimization algorithm to solve this model for GISR. The algorithm is in a concise framework that facilitates to be replicated into commonly used deep network structures. Thirdly, to reduce the information loss across iterative stages, the persistent memory mechanism is introduced to augment the information representation by exploiting the Long short-term memory unit (LSTM) in the image and feature spaces. In this way, a deep network with certain interpretation and high representation ability is built. Extensive experimental results validate the superiority of our method on a variety of GISR tasks, including Pan-sharpening, depth image super-resolution, and MR image super-resolution. Code will be released at https://github.com/manman1995/pansharpening.

The sequence of echoes detected by an active Synthetic Aperture Sonar (SAS) is coherently added in an appropriate way to produce an image with greatly enhanced resolution in the azimuth, or along-track direction when compared with an image obtained from a standard Side Looking Sonar (SLS). The SAS processing originates from the Synthetic Aperture Radar (SAR) concept. A complete introduction to SAR technique can be found in (Sherwin et al., 1962), (Walker, 1980), (Wiley, 1985) and (Curlander & McDonough, 1991). Raytheon was issued in 1969 a patent for a high-resolution seafloor imaging SAS (Walsh, 1969) and 1971 analyzed a potential system in terms of its resolution and signal-to-noise ratio. Cutrona was the first well-known radar specialist to point out how the various aspects of SAR could be translated to an underwater SAS (Cutrona, 1975). Hughes (1977) compared the performance of a standard SLS to an SAS and showed that the potential mapping rate for SAS was significantly higher than for side-looking sonar. At the time there was an assumption that the instability of the oceanic environment would prevent the formation of SAS imagery. Experimental work, which was performed by Williams (1976) and Christoff et al. (1982), refuted the instability worry. The verification of this assertion performed at higher frequencies by Gough & Hawkins (1989). Later, other concerns regarding the stability of the towed platform were also raised and some railor wire-guided trails where set up to avoid this extra complication. Nowadays there are a multiple of systems as hull mounted SAS systems, towed SAS systems and Autonomous Underwater Vehicles (AUV) systems. For further reading one can find an extended historical background of SAS in (Gough & Hawkins, 1997). Time and experience were needed to adapt SAR algorithms to SAS systems; the SAS systems use smaller radiating elements in proportion to the wavelength, which leads to higher radiation pattern of SAS with respect to SAR. The range migration effect on synthetic aperture processing is significant and pronounced in SAS imagery. An additional difference between SAR and SAS systems is the synthetic aperture time being greater in one order of magnitude in SAS, which leads to a phase corruption due to the medium fluctuations and platform instabilities. Typical synthetic aperture times for SAR are of the order of seconds with a medium coherence of some days, whereas for SAS the typical synthetic aperture time is of the order of several minutes with a similar medium coherence time (Marx et al. 2000). O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg

Improving the resolution of Synthetic Aperture Radar (SAR) is a major issue that has gained significant attention recently, especially in the squint mode where obtaining high resolution is challenging. Generally speaking, the azimuth and range resolutions can be improvement by applying matched filtering for compressing the signal. Signal compression is a major part of the well-known algorithms such as the Range Doppler algorithm (RDA) and the Chirp Scaling Algorithm (CSA) by which high positioning accuracy and high resolution are obtained. The transmitted signal by SAR is most often in the form of linear frequency modulation (LFM). However, the chirp signal is always generated in the SAR azimuth direction at the receiver. The squint of observation angle of the radar makes the chirp signal non-linear and non-stationary. Consequently, this study particularly focuses on the azimuth signal compression and instead of the Matched Filter (MF) based on Fourier Transform (FT), an alternative Matched Filter is used for signal compression on time-frequency domain that is known as Matched on Slope Filter (MSF). The Matched on Slope Filter improves the effectiveness of the compression in both squint and non-squint modes. The simulation results indicate that the proposed method improves the resolution and decreases the Peak to Side Lobe Ratio (PSLR) in the target detection.

Synthetic Aperture Radar (SAR) technology is capable to provide high resolution image data of earth surfaces from a moving vehicle. This causes large volumes of raw data. Many researchs were proposed about compressed radar imaging, which can reduce the sampling rate of the analog digital converter (ADC) on the receiver and eliminate the need of match filter on the radar receiver. Besides the advantages, there is a major problem that produces a large measurement matrix, which causes a very intensive matrix calculation. In this paper is studied a new approach to partial acquisition technique to reduce the amount of raw data using compressed sampling in both the azimuth and range and to reduce the computational load. The results showed that the reconstruction of SAR image using partial acquisition model has good resolution comparable to the conventional method (Range Doppler Algorithm). On a target of a ship, that represents a low level sparsity, a good reconstruction image could be achieved from a fewer number measurement. The method can speed up the computation time by a factor of 2.64 to 4.49 times faster than with a full acquisition matrix.

Imaging approach based on range-Doppler algorithm for monostatic SAR is extended to bistatic case. Because of that the transmitter and receiver are located separately, the range cell migration phenomena of bistatic SAR is more complicated than that of monostatic case. In this paper, a simple method of range cell migration correction is used instead of interpolation operation in traditional range-Doppler algorithm for monostatic SAR. Point scatter response, range and azimuth resolutions are also discussed. The simulation results are presented.

The main objective of Synthetic Aperture Radar (SAR) processing is converting the raw acquired data into pixels in the final processed image. The signal is spread in the range and azimuth directions, and the signal migrates to other range cells. Range Doppler Algorithm (RDA) is the most widely applied algorithm in SAR data processing. It performs range compression followed by azimuth compression to form the final reconstructed image. Unfortunately, SAR images suffer from many imperfections that will be studied and analyzed in this paper. Firstly, range compression introduces sidelobes that reduce the Signal-to-Noise Ratio (SNR). For this case, a comparison between three configurations of frequency modulated signals is presented. Moreover, the channel effect that causes degradations in the received signals is investigated and regularized de-convolution is used to reduce this effect. Finally, the random noise that causes a pixel-to-pixel variation and appears as a granular noise in the image will be reduced by applying a median filter to the reconstructed image.

In this paper, the shooting and bouncing ray (SBR) technique is employed to calculate the scattering of the cylinder cavity and by combining with range Doppler algorithms (RDA), the SAR image of the cylinder cavity is accurately obtained. Furthermore, the results reveal the effects of multi-path scattering mechanisms happened in the cavity, and also we give a detail explanation of the interference of multi-path scattering mechanisms on SAR imagery. And we point out for the first time that the cloud phenomenon in the down range is corresponding to focus mechanisms, which is totally different from that in the across range. Our present quantitative investigation provides guidelines for the SAR target recognition. (4 pages)