Robust SAR Waveform Design for Extended Target in Spectrally Dense Environments

The problem of ship detection based on superpixels in synthetic aperture radar (SAR) images is considered. Existing superpixel-based detectors are often developed according to the simple contrast between the gray-levels of ship targets and the sea clutter surrounding it. These detectors are only based on first-order statistics of superpixels and may suffer from deteriorative performance in low signal-to-clutter ratio (SCR) scenarios because of the blurry SAR images. In this paper, we propose a new detector for ship targets in SAR images based on the contrast of contextual Fisher vectors (CCFVs). CCFV provides more abundant contrast between ship targets and their surrounding background clutter on multi-order information and multi-Gaussian components. Experimental results on the measured SAR images show that, compared with commonly used detectors, the proposed CCFV-based detector achieves better performance especially for weak target detection in low SCR scenarios.

This paper is concerned with the application of robust control design concepts for the physical geometric design of electrohydraulic valves. Limitations of solenoid actuators have prevented single stage electrohydraulic valves which are simpler and more cost effective from being utilized in high flow rate and high bandwidth applications. Fluid flow force induced instability has been proposed as a means to alleviate the demand on the solenoid actuators. Previous research has demonstrated that simple changes in the valve geometry can be used to manipulate both the transient flow force as well as the steady flow force for this purpose. This paper considers the dimensional design of such ldquounstablerdquo valves to minimize the net steady flow force. The robust optimal design method, in which the design must be robust to uncertainties such as variations in operating pressure ranges and dynamic viscosity, etc., is proposed. By representing the original problem as a linear fractional transformation interconnection, the robust design problem is formulated into one of synthesizing an optimal controller for an appropriate static plant with a structured uncertainty. An algorithm for solving this design synthesis problem is proposed. A case study is conducted to compare the nominal optimal (without considering uncertainty) and the robust optimal designs. It is shown that viscosity effect is exclusively utilized in the nominal optimal design, whereas both the viscosity effect and the nonorifice flux effect are needed in the robust optimal design. The robust optimal design imposes smaller steady flow force on the spool than the nominal optimal design under perturbed situations. Based on the robust design method, an actual prototype design of the unstable valve has been developed.

<div class="section abstract"><div class="htmlview paragraph">Existing multi-axle steering system designs generally use the deterministic optimization method without considering the uncertainties during the design process; therefore an actual steering movement may deviate from the ideal movement calculated by some mathematical models. In order to make design results have less sensitive to the uncertainties in the design process, some uncertainties need be taken into account at the early design stage. This paper proposes a robust optimization design method for a double front axle steering system (DFASS) of heavy trucks based on Monte Carlo method. The DFASS consists of two trapezoidal steering mechanisms (TSM) and one rocker system, and the optimization objectives of DFASS include the minimum mean value and variance of the maximum turning angle error of the TSM and rocker system. In addition, the robust optimization model includes 13 design variables which are all geometry parameters of DFASS and represented by normal distribution. Through the orthogonal experiment, we obtain the important factors affecting optimization objectives and build the response surface models of optimization objective. Based on the response surfaces, robustness design of DFASS is performed. By simulating analysis, the improved robustness of DFASS based on robust design method is approved.</div></div>

The most distinctive characteristic of synthetic aperture radar (SAR) is that it can acquire data under all weather conditions and at all times. However, its coherent imaging mechanism introduces a great deal of speckle noise into SAR images, which makes the segmentation of target and shadow regions in SAR images very difficult. This paper proposes a new SAR image segmentation method based on wavelet decomposition and a constant false alarm rate (WD-CFAR). The WD-CFAR algorithm not only is insensitive to the speckle noise in SAR images but also can segment target and shadow regions simultaneously, and it is also able to effectively segment SAR images with a low signal-to-clutter ratio (SCR). Experiments were performed to assess the performance of the new algorithm on various SAR images. The experimental results show that the proposed method is effective and feasible and possesses good characteristics for general application.

Change detection with Synthetic Aperture Radar (SAR) image has wide application prospect, but an accurate registration is a very important precondition for most of the present change detection methods. However, the technology of image registration is still a hard work in the area of image processing especially for SAR image, so it is rather difficult to find a good SAR image registration method which should have both strong adaptability and high accuracy. This will restrict the application of change detection for SAR image to some extent. Thinking of the problem of registration accuracy for the present change detection methods, and according to the characteristics of man-made target in SAR image, a new change detection method with SAR image is presented in this paper, which is based on target detection. In this method, the change detection process is divided into two steps. First, the man-made targets in the images will be detected; second, we will compare the similarity between the relative position relationships of the man-made targets which have been detected from the two images, then the change of images will be decided from this result. As the change detection process is mainly dependent on the relative position relationships of the detected targets, the requirement to the accurate registration can be greatly reduced. The experimental result shows that this method is capable of detecting image changes with rough registration accuracy.

Target detection in SAR images using Bayesian Saliency and Morphological attribute profiles

A robust experimental design method was developed using a response surface methodology and models to facilitate the development process of retinol solid lipid nanoparticles (SLNs). The SLNs were evaluated to determine how different parameters including lipid and surfactant affect size and encapsulation efficiency. This was conducted using factorial analysis and a robust design (RD) method was used to achieve optimal formulations. Two models were developed based on the RD principle and both mean and variance of the response characteristics were estimated functionally using the least squares method. They proved useful in formulation studies aiming to develop optimum by allowing a systematic and reliable design method. A model for maximizing the overall desirability represented by the geometric mean of all objectives was found to provide a better solution. The newly designed method provides useful information to characterize significant factors and obtain optimum formulations, thereby allowing a systematic and reliable design method.

Existing superpixel-based detection algorithms for ship targets in synthetic aperture radar (SAR) images are often derived from the local contrast of intensities (i.e., the local contrast of the first-order information of superpixels) leading to deteriorating performance in low signal-to-clutter ratio (SCR) cases due to the low contrast between the intensities of targets and the clutter. In this article, we propose a new superpixel-based detector to improve the performance of ship target detection in SAR images via the local contrast of fisher vectors (LCFVs). The new LCFV-based detector exploits multiorder features of the superpixels based on the Gaussian mixture model (GMM) and accordingly improves the discrimination capability between the ship targets and the sea clutter, especially in low SCR cases. Experimental results demonstrate that the proposed LCFV-based detection algorithm provides better detection performance than the commonly used detection algorithms.

In the reliability designing procedure of the vehicle components, when the distribution styles of the random variables are unknown or non-normal distribution, the result evaluated contains great error or even is wrong if the reliability value R is larger than 1 by using the existent method, in which case the formula is necessary to be revised. This is obviously inconvenient for programming. Combining reliability-based optimization theory, robust designing method and reliability based sensitivity analysis, a new method for reliability robust designing is proposed. Therefore the influence level of the designing parameters’ changing to the reliability of vehicle components can be obtained. The reliability sensitivity with respect to design parameters is viewed as a sub-objective function in the multi-objective optimization problem satisfying reliability constraints. Given the first four moments of basic random variables, a fourth-moment technique and the proposed optimization procedure can obtain reliability-based robust design of automobile components with non-normal distribution parameters accurately and quickly. By using the proposed method, the distribution style of the random parameters is relaxed. Therefore it is much closer to the actual reliability problems. The numerical examples indicate the following: (1) The reliability value obtained by the robust method proposed increases (>0.04%) comparing to the value obtained by the ordinary optimization algorithm; (2) The absolute value of reliability-based sensitivity decreases (>0.01%), and the robustness of the products’ quality is improved accordingly. Utilizing the reliability-based optimization and robust design method in the reliability designing procedure reduces the manufacture cost and provides the theoretical basis for the reliability and robust design of the vehicle components.

A novel visual attention method for target detection from SAR images

Target vibrations introduce nonstationary phase modulation, which is termed the micro-Doppler effect, into returned synthetic aperture radar (SAR) signals. This causes artifacts, or ghost targets, which appear near vibrating targets in reconstructed SAR images. Recently, a vibration estimation method based on the discrete fractional Fourier transform (DFrFT) has been developed. This method is capable of estimating the instantaneous vibration accelerations and vibration frequencies. In this paper, a deghosting method for vibrating targets in SAR images is proposed. For single-component vibrations, this method first exploits the estimation results provided by the DFrFT-based vibration estimation method to reconstruct the instantaneous vibration displacements. A reference signal, whose phase is modulated by the estimated vibration displacements, is then synthesized to compensate for the vibration-induced phase modulation in returned SAR signals before forming the SAR image. The performance of the proposed method with respect to the signal-to-noise and signalto-clutter ratios is analyzed using simulations. Experimental results using the Lynx SAR system show a substantial reduction in ghosting caused by a 1.5-cm 0.8-Hz target vibration in a true SAR image.

Synthetic aperture radar (SAR) is an important means for target surveillance through reconstructing the microwave image of the observation area. However, under the condition of low signal-to-clutter ratio (SCR), such as a strong sea clutter situation, it is difficult to surveil targets from SAR images acquired by the traditional matched filter-based imaging methods. To improve the target surveillance performance of SAR, this article proposes a target-oriented SAR imaging method, which can enhance the desired target and improve the SCR in the reconstructed SAR images. By separating the target area from the clutter area, we first establish a target-oriented SAR imaging model, where the generalized regularization is used to characterize the features of the target, contributing to the improvement of SCR in the reconstructed image. Then, the imaging model is solved through a deep network, MF-ADMM-Net, which is obtained by unfolding an alternating direction method of multipliers (ADMM)-based iterative solution. In addition, the training strategy is formulated with the consideration of complex values. Experiments are conducted to verify the performance of image reconstruction and SCR improvement of the proposed method, and comparisons show the superiority of MF-ADMM-Net in effect and efficiency.

In this paper, a joint design of both transmit waveform and imaging filters for synthetic aperture radar (SAR) is proposed to improve its information acquisition capability. First, the mutual information between SAR image and target scattering characteristics is considered as the performance metric, and its equivalent analytical version is derived in the 2-D frequency domain. The design is formulated as an optimization problem with an energy condition and a similarity constraint. Then, to tackle the resultant non-convex problem, by referring to the Dinkelbach's method, an algorithm is derived to find the desired solution via a cyclic maximization procedure alternating between three subproblems. Based on minorization-maximization, a unified optimization method with an increasing penalty on constraint violation is proposed to solve all subproblems. Convergence of the developed algorithm is analytically proved. Finally, numerical examples are presented to demonstrate the effectiveness of the proposed design.

Knowledge about ship positions plays a critical role in a wide range of maritime applications. To improve the performance of ship detector in SAR image, an effective strategy is improving the signal-to-clutter ratio (SCR) before conducting detection. In this paper, we present a comparative study on methods of improving SCR, including power-law scaling (PLS), max-mean and max-median filter (MMF1 and MMF2), method of wavelet transform (TWT), traditional SPAN detector, reflection symmetric metric (RSM), scattering mechanism metric (SMM). The ability of SCR improvement to SAR image and ship detection performance associated with cell- averaging CFAR (CA-CFAR) of different methods are evaluated on two real SAR data.

Increasing competition in the global marketplace demands products be adaptable to the changes of functional requirements, operation environments, and technology advancement. Adaptable design can be used for developing adaptable products that can be easily changed, such as reconfigured or upgraded, in the product operation stage. In addition, since the uncertainties in product parameters and environment parameters usually lead to the variations of functional performance, robust design has to be carried out to identify the design whose functional performance is the least sensitive to the variations of product and environment parameters. In this research, a robust adaptable design method is developed to identify the product whose parameter values can be changed in the product operation stage. First, a mathematical model is established to describe the relationships among product functional performances, parameters, and variations of these parameters. Then, an optimization-based design model is developed to achieve the robust adaptable design. A case study has also been developed to demonstrate the effectiveness of the newly developed robust adaptable design method.