Identifiability in wind estimation from scatterometer measurements

The problem of identifiability of a wind vector that is estimated from wind scatterometer measurements of the radar backscatter of the ocean's surface is addressed. The traditional wind estimation approach produces multiple estimates of the wind direction. A second processing step, known as dealiasing or ambiguity removal, is used to select a single wind estimate from these multiple solutions. Dealiasing is typically based on various ad hoc considerations. The traditional wind estimation approach results in multiple solutions associated with local minima in an objective function formed from the noisy backscatter measurements. The authors discuss the question of the uniqueness of the wind vector estimates resulting from this intuitive approach     

Retrieving snowpack properties and accumulation estimates from a combination of SAR and scatterometer measurements

This study combines two satellite radar techniques, low-resolution C-/Ku-band scatterometer and high-resolution C-band synthetic aperture radar (SAR) for glaciological studies, in particular mass-balance estimations. Three parameters expressing the mean backscattering and its dependency on azimuth and incidence angle are used to describe and classify the Antarctic ice sheets backscattering behavior. Simple linear regression analyses are carried out between ground truth accumulation data and the SAR backscattering coefficient along continuous profile lines. From this we parameterize the accumulation rate separately for certain snow pack regimes. We find that SAR data can be used to map mass-balance changes, however only within limited areas. Applying this method therefore generally requires accurate ground truth for regional calibration together with additional information regarding mean air temperature or elevation. This investigation focuses on the area of Dronning Maud Land, Antarctica. We present the first high-resolution accumulation map based on SAR data for the surrounding area of the EPICA deep ice core drilling site Kohnen, which is compared to reliable ground truth records as well as to a surface-mass-balance map interpolated from these at low resolution.     

SIH: segmented intensity histogram for orientation estimation in image matching

In this paper, we propose a fast and effective new method to reduce the overhead cost of orientation estimation. The proposed method uses the summation of intensity values from segments of image patches and forms a histogram based on those values. As a result, it is faster than SIFT-like algorithms because it does not require computation of gradient orientations and magnitudes. Also, it is as fast as other intensity-based algorithms with better image matching performance. Proposed method could be easily integrated to any image matching algorithms. Test results indicate that SIFT integrated with proposed orientation estimation method produces comparable results as the original multi-angle SIFT algorithm with less execution time.     

Error estimation for numerical differential equations

Given a capable human being and a computer, it is possible to make an approximation to the solution of a nonlinear differential equation. However, under the (usually correct) assumption that the equation is analytically intractable, the result of the computation is not the exact solution; indeed it may be so far from the exact solution as to be completely useless. We are interested in the relationship between the effort expended by the human and the computer, and the duality of the computed approximation to a partial or ordinary differential equation. To be specific, we would like to think in terms of a cost-benefit analysis. The cost of the computation is a combination of the human effort and computer resources used to obtain the approximation. The benefit includes, of course, the computed approximation, but it also includes an estimate of the quality of the approximation, that is, an error estimate. It is our opinion that in computational science, as with the experimental sciences, results should always be presented with some estimate of their accuracy. In addition, however, there is another facet to error estimation: one cannot even attempt a cost-benefit analysis or efficiency comparison of methods without an error estimate to evaluate the results     

Error probability for reduced-state sequence estimation

The performance of ideal reduced-state sequence estimation (RSSE) (without error propagation) is known as a good approximation to the performance of real RSSE. In the literature, the minimum distance of ideal RSSE has been employed for approximating the error probability of real RSSE. However, this approximation can be very poor, even though the system has a large signal-to-noise ratio. In this work, a union upper bound on the error probability for ideal RSSE is used to approximate the true error probability. This union bound provides a better approximation than the minimum distance. A new method based on a stack algorithm and a subset-error state diagram is proposed for calculating this union bound. The stack algorithm is employed because it provides a good tradeoff between computer memory and computing time     

Error bounds for convex parameter estimation

We evaluate the accuracy of sparsity-based estimation methods inspired from compressed sensing. Typical estimation approaches consist of minimizing a non-convex cost function that exhibits local minima, and require excessive computational resources. A tractable alternative relies on a sparse representation of the observation vector using a large dictionary matrix and a convex cost function. This estimation approach converts the intractable high-dimensional non-convex problem into a simpler convex problem with reduced dimension. Unfortunately, the advantages come at the expense of increased estimation error. Therefore, an evaluation of the estimation error is of considerable interest. We consider the case of estimating a single parameter vector, and provide upper bounds on the achievable accuracy. The theoretical results are corroborated by simulations.     

Error estimates and adaptive procedures for the two-dimensionalfinite element method

An a posteriori error estimate method, the element residual method (ERM), was investigated, and used to obtain local as well as global error estimates for finite element method (FEM) solutions of static electric field problems. The local error estimates were used to adapt the FEM meshes, leading to improved convergence rates for FEM solutions     

Validating a scatterometer wind algorithm for ERS-1 SAR

The ocean surface wind field is observed from space operationally using scatterometry. The European Space Agency's (ESAs) ERS-1 satellite scatterometer routinely produces a wind product that is assimilated into forecast models. Scatterometry cannot give accurate wind estimates close to land, however, because the field of view of a spaceborne scatterometer is on the order of 50 km. Side lobe contamination, due to the large contrast in backscatter between land and water, compounds the problem. Synthetic aperture radar (SAR) can provide wind speed and direction estimates on a finer scale, so that high-resolution wind fields can be constructed near shore. An algorithm has been developed that uses the spectral expression of wind in SAR imagery to estimate wind direction and calibrated backscatter to estimate wind strength. Three versions, based on C-band scatterometer algorithms, are evaluated for accuracy in potential operational use. Algorithm estimates are compared with wind measurements from buoys in the Gulf of Alaska, Bering Strait, and off the Pacific Northwest coast by using a data set of 61 near-coincident buoy and ERS-1 SAR observations. Representative figures for the accuracy of the algorithm are ±2 m/s for wind speed and ±37° for wind direction at a 25-km spatial resolution     

Error propagation in blind sequence estimation

We demonstrate how error propagation may occur with blind sequence estimation based on the Viterbi algorithm (BSE-VA). We further show that differential encoding can be used to alleviate this error propagation in blind sequence estimation. In addition, we compare the BSE-VA with a much simpler differential correlation approach (DCA) method for differentially encoded input symbols     

Error bounds for stochastic estimation of signal parameters

This paper is concerned with stochastic-approximation algorithms for estimating signal parameters. Emphasis will be on the performance of the algorithm for a finite number of observations as opposed to the asymptotic convergence rate. We use as an upper bound a result due to Dvoretzky. A lower bound on the average mean-square error is derived. This new bound is based on the Cramér-Rao inequality. The conventional Cramér-Rao bound is not directly applicable, because it requires the knowledge of the bias function, which is difficult to find in a recursive estimation scheme. To avoid this difficulty, we introduce the concept of most favorable bias function and use the calculus of variations to derive the lower bound. The new bound also serves as a standard to judge the merits of the stochastic-estimation algorithm, since under some general conditions no estimate can yield smaller error. It is shown that under some conditions the two bounds are nearly equal, and hence the algorithm is near optimal. The asymptotic efficiency of the algorithm is compared with Sakrison's result. A stochastic-estimation algorithm is derived for estimating Doppler frequency, and performance curves in terms of the error bounds are presented.     

JPEG quantization step estimation with coefficient histogram and spectrum analyses

This paper proposes a new method for estimating quantization steps (QSs) from an image that has been previously JPEG-compressed and stored in a lossless format. In this method, DCT coefficients of each frequency band of JPEG-compressed image are aggregated in the QS and its multiples. The entire estimation process can be grouped into two categories: alternating and direct current bands. Considering that DCT coefficients under different QSs show different periodicity, QS estimation for each band is then further divided into three steps, which involve identifying whether the QS is one, two, or another value. For each step, the periodicity of DCT coefficients can be well exploited with the analyses of the DCT-coefficient histogram and its corresponding frequency magnitude spectrum. Experimental results demonstrate the efficacy of the proposed method and the superiority in QS estimation for previously JPEG-compressed images, especially in the case that the actual QSs are higher than two.     

基于视点特征直方图的激光点云模型的位姿估计

提出一种基于视点特征直方图的点云模型位姿估计 算法。首先在目标物体周围采 集三维点云,拼接后获得物体的完整点云模型;然后对点云模型计算其视点特征直方图, 构建特征数据 库;对待估计点云同样计算其特征直方图,使用KNN算法在数据库中搜索与之最接近的位 姿作为初始位 姿估计值;最后使用迭代最近点(ICP)算法将待估计点云精确配准到模型点云,从而获得坐 标系之间的相 对位姿。实验表明,这种方法对于物体位姿识别有很强的稳健性,能很好实现目标物体的 三维位姿计算。     

Radar backscatter measurement accuracy for a spaceborne pencil-beamwind scatterometer with transmit modulation

Scatterometers are remote sensing radars designed to measure near-surface winds over the ocean. The difficulties of accommodating traditional fan-beam scatterometers on spacecraft has lead to the development of a scanning pencil-beam instrument known as SeaWinds. SeaWinds will be part of the Japanese Advanced Earth Observing Satellite II (ADEOS-II) to be launched in 1999. To analyze the performance of the SeaWinds design, a new expression for the measurement accuracy of a pencil-beam system is required. In this paper the authors derive a general expression for the backscatter measurement accuracy for a pencil-beam scatterometer which includes the effects of transmit signal modulation with simple power detection. Both separate and simultaneous signal+noise and noise-only measurements are considered. The utility of the new expression for scatterometer design tradeoffs is demonstrated using a simplified geometry. A separate paper, ibid., 1997, describes detailed tradeoffs made to develop the SeaWinds design     

Mutual information-based CT-MR brain image registration using generalized partial volume joint histogram estimation

Mutual information (MI)-based image registration has been found to be quite effective in many medical imaging applications. To determine the MI between two images, the joint histogram of the two images is required. In the literature, linear interpolation and partial volume interpolation (PVI) are often used while estimating the joint histogram for registration purposes. It has been shown that joint histogram estimation through these two interpolation methods may introduce artifacts in the MI registration function that hamper the optimization process and influence the registration accuracy. In this paper, we present a new joint histogram estimation scheme called generalized partial volume estimation (GPVE). It turns out that the PVI method is a special case of the GPVE procedure. We have implemented our algorithm on the clinically obtained brain computed tomography and magnetic resonance image data furnished by Vanderbilt University. Our experimental results show that, by properly choosing the kernel functions, the GPVE algorithm significantly reduces the interpolation-induced artifacts and, in cases that the artifacts clearly affect registration accuracy, the registration accuracy is improved.     

A C-band scatterometer for remote sensing the air-sea interface

An airborne C-band scatterometer system (C-Scat) has been developed to remotely sense ocean surface winds and improve upon the present understanding of the relationship between normalized radar cross section (NRCS) and ocean surface roughness influences such as wind speed and direction, wave height and slope, and the air-sea temperature difference. The scatterometer utilizes a unique frequency-steered microstrip array antenna that is installed beneath the fuselage of an airplane. The antenna is electronically scanned in elevation, from 20° to 50° off-nadir, and mechanically spins in azimuth. The system is capable of measuring ocean surface NRCS from altitudes as high as 25000 ft. The system is divided into four subsystems: the transmitter and receiver, the spinning antenna, the computer control and data acquisition subsystem, and the digital and analog interface electronics     

基于光流估计的整帧恢复算法

探讨了在无线环境中可能出现的整帧丢失情况，并利用光流估计的思想，提出了一种有效的双向估计恢复算法。实验结果证明，提出的改进优化算法能较好地提高恢复图像性能，并具有一定的实用价值。     

一种测量光学表面粗糙度的全积分散射仪

运用全积分散射理论,结合积分球的光度特性,研制了一种用于粗糙度测量的比较法全积分散射仪.用原子力显微镜分析了用相同的抛光工艺制备的三片超光滑硅片的表面粗糙度,其结果均在0.14～0.19 nm之间,说明了此抛光工艺的稳定性;用原子力显微镜测量值为0.143 nm均方根粗糙度的超光滑硅片作为参考样片,比较了其他三片相同工艺制备的硅表面的全积分散射测量结果,结果显示与标准片的测量结果非常接近,均在0.14～O.18 nm之间,验证了全积分散射法的合理性;进一步分析了锗、铝、碳化硅等表面的全积分散射测量结果,结果呈现出明显的差异,说明该方法具有较高的灵敏度和较大的动态范围.     

An experimental study of a pulse compression scatterometer system

The ideal scatterometer, operating from either an aircraft or a satellite platform, should be capable of making rapid, accurate estimates of the sea backscatter cross sectionsigmaover as wide a range of grazing anglespsias possible. Efficient operation over a large range of grazing angles is desirable because 1)sigmabehavior for90deg geq psi geq 70degyields rms gravity wave slope information and is an indicator of sea state and 2)sigmabehavior for70deg geq psi geq 0degyields data on surface wind magnitude and direction as well as information about the power spectrum of the sea. A "hybrid" estimation procedure has been developed for pulse compression radars which uses both frequency and spatially decorrelated samples ofsigmato provide an unbiased estimate ofsigmahaving minimum variance over the entire range of grazing angles for which radar reception is not noise-limited.