Improved slant range model and imaging method for geosynchronous SAR

In studying image formation methods of the spaceborne synthetic aperture radar (SAR), we utilize its modeling and simulation (M&S) to generate its realistic simulated rawdata. Especially, for the spaceborne spotlight SAR, we perform M&S that reflects its real characteristics, and get rawdata that are almost identical to one acquired by the real SAR sensor. Particularly, operations of the spaceborne spotlight SAR are simulated based on models of its dynamics and geometry related to timeline, orbital state vector, antenna beam pattern, azimuth beam steering, and etc. In addition, the target observation of it is modeled as evaluating observation angles related to point targets within the acquisition time. Finally, based on the received echo signal model, rawdata are simulated for point targets taking into account its real operation. For the high resolution SAR image formation, simulated rawdata are focused with the extended chirp scaling algorithm. Especially, its range cell migration (RCM) factor is the key one for the exact range cell migration correction. In order to do it accurately, the Doppler frequency and the effective velocity have to be calculated correctly for all range sample bins. For precise processing, we suggest the method to analyze them using orbital state vectors and scene coordinates based on two way slant range model. In experiments, system parameters and imaging scenarios to simulate rawdata acquisition of the spaceborne spotlight SAR system are defined. The processing results for realistic simulated rawdata of it are presented to evaluate the performance and the effectiveness of proposed methods. Its results show that suggested methods are applicable to form the high resolution spaceborne spotlight SAR image.

In this paper, considering the relative motion between satellite and earth during signal propagation time, the accurate analysis method for propagation slant range is presented in geosynchronous SAR. Furthermore, the difference between accurate analysis method and `Stop-and-Go' assumption is analytically obtained. Meanwhile based on the derived accurate slant range model, it is found that the corresponding range migration correction and azimuth reference function must consider the high order term, and therefore the modified SPECAN algorithm is proposed. The simulation results verify the correctness of `Stop-and-Go' assumption error derivation and SPECAN algorithm modification.

ABSTRACTGeosynchronous spaceborne-airborne bistatic synthetic aperture radar (GEO SA-Bi SAR), which employs a geosynchronous orbit (GEO) satellite as a transmitter and an aeroplane that works on the ‘receive-only’ mode as a receiver, is distinct from general bistatic synthetic aperture radar (SAR) systems. In this paper, a GEO SA-Bi SAR system with multi-receive antenna is consider for marine surveillance. First, ship position is estimated in the configuration of GEO SA-Bi SAR with multi-receive antenna, using maximum likelihood (ML) method. Then, according to the estimation result, sea clutter in the signal of a moving point target is suppressed by adaptive beamforming (ABF). Moreover, the performance of our method is analysed and studied in terms of the point target imaging. The simulation results demonstrate the correctness and effectiveness of the proposed method.

All radar targets can be classified into point and complex targets. A point target is a convenient model object commonly used in radar science and practice to solve certain types of problems. It is defined as a target located at distance R from a radar at the viewing point '0', which scatters the incident radar radiation isotropically. For such a target, the equiphase surface is a sphere with the centre at '0'. In practice, most radar targets refer to the class of complex targets. In spite of a great variety of particular targets, we can offer a common criterion for their classification. This criterion is based on the relationship between the maximum target size and the radar resolving power in the coordinate space of the parameters R, a, β and R, which are the range, the azimuth, the elevation angle and the radial velocity of the target, respectively.

Recently, various deep learning-based high dynamic range (HDR) imaging methods have been proposed to overcome the performance limitation of simply merging or fusion of multiple low dynamic range (LDR) images. However, early deep learning-based methods did not fully exploit the inter-image relationship between different exposures. T o consider inter-image features without the dataset dependency, this paper presents a novel HDR method using three-dimensional residual dense network (3D RDN). We first reconstruct a set of differently exposed LDR images in video format. Next, we take the reconstructed video as an input to the 3D RDN, which generates an HDR image as output. Experimental results show that the proposed method provides higher score compared with state-of-the-arts HDR methods. To the best of authors knowledge, the proposed work first adopted the 3D RDN in the field of HDR imaging using more than three LDR images without any additional pre-or post-processing.

In this paper, we present a novel high dynamic range (HDR) imaging method using a single input image. Conventional multiple image-based HDR methods are successful only on condition that there is no motion in the scene during the acquisition of multiple, differently exposed low dynamic range (LDR) images. If these constraints are not satisfied, a ghost artifact is produced in the resulting HDR image. In order to overcome these limitations, we generate multiple, differently exposed LDR images from a single input image. We call these multiple images a set of layered exposed (LE) images. In order to generate an appropriate set of LE images, the proposed method divides input image into 9 subregions and computes local mean in each subregion to estimate the minimum and maximum local mean. The estimated local means become ranges of histogram equalization (HE). HDR image is generated by fusing differently exposed LE images. More specifically, given a set of LE images, we perform weighted fusion to produce the resulting HDR image, which is inherently free from ghost artifacts since all LE images are geometrically identical. Experimental results show that the proposed method outperforms the existing algorithms in the sense of both removing ghost artifacts and enhancement of image contrast.

Most existing nighttime visibility enhancement methods focus on low light. Night images, however, do not only suffer from low light, but also from man-made light effects such as glow, glare, floodlight, etc. Hence, when the existing nighttime visibility enhancement methods are applied to these images, they intensify the effects, degrading the visibility even further. High dynamic range (HDR) imaging methods can address the low light and over-exposed regions, however they cannot remove the light effects, and thus cannot enhance the visibility in the affected regions. In this paper, given a single nighttime image as input, our goal is to enhance its visibility by increasing the dynamic range of the intensity, and thus can boost the intensity of the low light regions, and at the same time, suppress the light effects (glow, glare) simultaneously. First, we use a network to estimate the camera response function (CRF) from the input image to linearise the image. Second, we decompose the linearised image into low-frequency (LF) and high-frequency (HF) feature maps that are processed separately through two networks for light effects suppression and noise removal respectively. Third, we use a network to increase the dynamic range of the processed LF feature maps, which are then combined with the processed HF feature maps to generate the final output that has increased dynamic range and suppressed light effects. Our experiments show the effectiveness of our method in comparison with the state-of-the-art nighttime visibility enhancement methods.

Azimuth multi-channel (AMC) synthetic aperture radar (SAR) systems can achieve high-resolution and wide-swath (HRWS) imaging, widely used in marine monitoring. However, for the moving ship targets irradiated by the AMC HRWS SAR system, false targets and defocus will appear in HRWS images caused by their motion. The existing methods behave poorly in case of low signal-to-noise ratio and channel non-uniformity. To improve the quality of HRWS images, the letter proposes a moving ship target imaging method based on spectrum energy distribution. Firstly, radial velocity of ship target is estimated by energy distribution factor parameter constructed by spectrum energy distribution. Secondly, the azimuth velocity is calculated by the phase difference between adjacent channels. Meanwhile, the importance of channel phase error on imaging is demonstrated. Finally, echo compensation and imaging method are performed to get a clear and focused image. Simulation experiments on point targets and area targets verify the effectivity of the proposed method. The proposed method can effectively suppress the false ship target, whose maximum energy is reduced from about −20 dB to below −40 dB, providing clearer images for the various application of different users.

Over the past several years, we have been studying the problem of optimally rotating a rigid sphere about its center, where the rotation is actuated by a triplet of external torques acting on the body. The control objective is to repeatedly direct a suitable radial vector, called the gaze vector, towards a stationary point target in IR3. The orientation of the sphere is constrained to lie in a suitable submanifold of SO(3). Historically, the constrained rotational movements were studied by physiologists in the nineteenth century, interested in eye and head movements. In this paper we revisit the gaze control problem, where two visual sensors, are tasked to simultaneously stare at a point target in the visual space. The target position changes discretely and the problem we consider is how to reorient the gaze directions of the sensors, along the optimal pathway of the human eyes, to the new location of the target. This is done by first solving an optimal control problem on the human binocular system. Next, we use these optimal control and show that a pan-tilt system can be controlled to follow the gaze trajectory of the human eye requiring a nonlinear static feedback of the pan and tilt angles and their derivatives. Our problem formulation uses a new Riemannian geometric description of the orientation space. The paper also introduces a new, pyramid based interpolation method, to implement the optimal controller.

A weighted hybrid indoor positioning method based on path loss exponent estimation

A new filtering method is proposed to accurately estimate target state via decreasing the nonlinearity between radar polar measurements (or spherical measurements in three-dimensional (3D) radar) and target position in Cartesian coordinate. The degree of linearity is quantified here by utilizing correlation coefficient and Taylor series expansion. With the proposed method, the original measurements are converted from polar or spherical coordinate to a carefully chosen Cartesian coordinate system that is obtained by coordinate rotation transformation to maximize the linearity degree of the conversion function from polar/spherical to Cartesian coordinate. Then the target state is filtered along each axis of the chosen Cartesian coordinate. This method is compared with extended Kalman filter (EKF), Converted Measurement Kalman filter (CMKF), unscented Kalman filter (UKF) as well as Decoupled Converted Measurement Kalman filter (DECMKF). This new method provides highly accurate position and velocity with consistent estimation.

High dynamic range imaging (HDRI) methods in computational photography address situations where the dynamic range of a scene exceeds what can be captured by an image sensor in a single exposure. HDRI techniques have also been used to construct radiance maps in measurement applications; unfortunately, the design and evaluation of HDRI algorithms for use in these applications have received little attention. In this paper, we develop a novel HDRI technique based on pixel-by-pixel Kalman filtering and evaluate its performance using objective metrics that this paper also introduces. In the presented experiments, this new technique achieves as much as 9.4-dB improvement in signal-to-noise ratio and can achieve as much as a 29% improvement in radiometric accuracy over a classic method.

Significance:While shortwave infrared (SWIR) imaging provides superior tissue penetration and reduced autofluorescence for preclinical applications, quantitative fluorescence analysis is hindered by the limited dynamic range of InGaAs cameras, forcing a focus on either bright or dim anatomical features.Aim:We develop a high dynamic range (HDR) imaging method specifically adapted for the high-noise characteristics of InGaAs detectors to enable quantitative fluorescence imaging across wide intensity ranges. We demonstrate that one-time camera calibration based on a series of images encompassing the range of radiance intensities enables all subsequent image processing.Approach:We modified classical HDR algorithms with exposure-time-dependent dark current subtraction, preprocessing to exclude saturated and noisy pixels before camera response function recovery, and dynamic weighting range adjustment to account for shrinking intensity ranges at longer exposures. HDR image processing effects on preclinical imaging outcomes were analyzed using indocyanine green and SWIR-emitting PbS/CdS quantum dots in mouse models.Results:HDR imaging achieved a 22 dB improvement in dynamic range over single exposures, enabling simultaneous quantification across more than three orders of magnitude of fluorophore concentration. In vivo studies showed improvements in contrast-to-noise ratios across all anatomical features, with improvements in vascular contrast while maintaining quantitative accuracy. After one-time camera calibrations, this approach enables rapid processing of subsequent datasets.Conclusions:This software-based HDR SWIR imaging approach eliminates exposure parameter optimization and enables comprehensive biodistribution analysis across all anatomical structures from a single acquisition sequence, significantly streamlining preclinical imaging workflows while preserving quantitative accuracy.

Enhancement of noisy image data is a very challenging issue in many research and application areas. In the last few years, non-linear filters, feature extraction, high dynamic range (HDR) imaging methods based on soft computing models have been shown to be very effective in removing noise without destroying the useful information contained in the image data. Although, to distinguish among noise and useful information is not an easy task and may highly depend on the situation and aim of the processing. In this chapter new image processing techniques are introduced in the field of image quality improvement, thus contributing to the variety of advantageous possibilities to be applied. The main intentions of the presented algorithms are (1) to improve the quality of the image from the point of view of the aim of the processing, (2) to support the performance, and parallel with it (3) to decrease the complexity of further processing using the results of the image processing phase.

Inspired by the extensive application of terahertz (THz) imaging technologies in the field of aerospace, we exploit a THz frequency modulated continuous-wave imaging method with continuous wavelet transform (CWT) algorithm to detect a multilayer heat shield made of special materials. This method uses the frequency modulation continuous-wave system to catch the reflected THz signal and then process the image data by the CWT with different basis functions. By calculating the sizes of the defects area in the final images and then comparing the results with real samples, a practical high-precision THz imaging method is demonstrated. Our method can be an effective tool for the THz nondestructive testing of composites, drugs, and some cultural heritages.