Xiaolei Lv

Data-driven inverse dynamics for human motion

Inverse dynamics is an important and challenging problem in human motion modeling, synthesis and simulation, as well as in robotics and biomechanics. Previous solutions to inverse dynamics are often noisy and ambiguous particularly when double stances occur. In this paper, we present a novel inverse dynamics method that accurately reconstructs biomechanically valid contact information, including center of pressure, contact forces, torsional torques and internal joint torques from input kinematic human motion data. Our key idea is to apply statistical modeling techniques to a set of preprocessed human kinematic and dynamic motion data captured by a combination of an optical motion capture system, pressure insoles and force plates. We formulate the data-driven inverse dynamics problem in a maximum a posteriori (MAP) framework by estimating the most likely contact information and internal joint torques that are consistent with input kinematic motion data. We construct a low-dimensional data-driven prior model for contact information and internal joint torques to reduce ambiguity of inverse dynamics for human motion. We demonstrate the accuracy of our method on a wide variety of human movements including walking, jumping, running, turning and hopping and achieve state-of-the-art accuracy in our comparison against alternative methods. In addition, we discuss how to extend the data-driven inverse dynamics framework to motion editing, filtering and motion control.

Study on a Novel Multiple Elevation Beam Technique for HRWS SAR System

With the improvement of resolution and image swath, the received data amount of spaceborne synthetic aperture radar (SAR) system is increasingly large and imposes more stringent requirement for the satellite payload and transmission link. In this paper, a novel multiple elevation beam (MEB) SAR and the processing scheme are studied to reduce the data amount. The detailed system design and the main procedures are described, and the explicit mathematic expression of the scheme is derived. Furthermore, an exemplary SAR system is provided and the simulation results are obtained to confirm the effectiveness of our proposal. Also, the performance of the MEB SAR system is analyzed in depth, from the perspectives of data amount, signal to noise ratio (SNR), and range ambiguity to signal ratio (RASR).

An InSAR Fine Registration Algorithm Using Uniform Tie Points Based on Voronoi Diagram

Interferometric synthetic aperture radar (InSAR) image coregistration is a nontrivial task for its skew and distorted image pair, especially in severe decorrelated areas. In this letter, a new InSAR coregistration method, which considers both the coherence of the reference points and their topographical distribution, is proposed to perform accurate coregistration. First, a conventional cross-correlation registration method is performed, and a number of high coherent points are extracted as the reference points. Then, some well-distributed reference points are selected by utilizing the Voronoi diagram-based distribution optimization algorithm. Their subpixel correspondences are selected by finding the maximum values of the cross-correlation functions. Some singular correspondences are rejected by the random sample consensus method. Based on these accurate correspondences, the parameters of polynomial mapping function are estimated via the weighted least squares method. It improves the accuracy of geometrical mapping functions and overcomes the limitation of the conventional registration method when the reference points locate in low coherent area. C-band airborne repeat-pass acquired SAR data with 0.5-m resolution are used to validate the proposed algorithm by comparing with the conventional registration algorithm in accuracy. Experimental results prove the effectiveness of the proposed algorithm.

3D surface reconstruction of layover areas in continuous terrain for multi-baseline SAR interferometry using a curve model

It has become a field of intensive research to exploit multi-baseline synthetic aperture radar (SAR) to solve layover effects. However, the traditional layover model is not suitable for 3D surface reconstruction in the case of continuous layover terrain for two major reasons. First, the continuous terrain cannot be precisely described by the traditional layover model. Second, the traditional layover model is formalized for a single range-azimuth resolution cell and does not take full advantage of the relationship among the neighbouring scatterers, producing ragged reconstruction results with large terrain bias. In order to solve these problems, we establish a new curve model and propose a corresponding layover solution method. Rather than rebuilding the layover area in each independent range-azimuth resolution cell, this method reconstructs it as a whole. It exploits both the multi-baseline SAR signal and a priori information of the continuous terrain to achieve better performance. In the final part of this article, the proposed method is applied to 3D surface reconstruction using simulated data and Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) data in comparison with the traditional layover model-based solution method. It is found that the proposed method leads to considerable improvements with regard to accuracy and robustness.

An arc-clustering-based phase ambiguity estimation method for persistent scatterer interferometry

ABSTRACT The reliability of persistent scatterer interferometry (PSI) is directly related to the accurate phase unwrapping (PU) of the 3-D (2-D space and time) sparse data stack. Phase ambiguities estimated using temporal phase unwrapping (TPU) are crucially exploited in most 3-D PU algorithms. However, most TPU methods are not noise-robust owing to the independent processing of each arc in the persistent scatterer network. To solve this problem, the cluster-analysis-based PU method for digital elevation model (DEM) construction is adapted to the phase ambiguity estimation problem for PSI in this letter. First, for each arc, the interior relation among phase ambiguities in multiple interferograms is derived to be linear and can be represented by an intercept vector. Subsequently, arcs with the same ambiguities are clustered according to their intercept vectors because arcs with identical phase ambiguities are verified to have the same intercept vectors. Moreover, a more reliable intercept vector can be calculated for each arc cluster by combining the intercept vectors within the cluster. Finally, more precise phase differences are estimated for each cluster using the corresponding intercept vector so that all the arcs are better unwrapped. Both the simulated and real experiments indicate the effectiveness and superiority of the proposed algorithm.

A Speckle Filtering Method Based on Hypothesis Testing for Time-Series SAR Images

To improve the suppression effect for the speckle noise of synthetic aperture radar (SAR) images and the ability of spatiotemporal information preservation of the filtered image without losing the spatial resolution, a novel multitemporal filtering method based on hypothesis testing is proposed in this paper. A framework of a two-step similarity measure strategy is adopted to further enhance the filtering results. Firstly, bi-date analysis using a two-sample Kolmogorov-Smirnov (KS) test is conducted in step 1 to extract homogeneous patches for 3-D patch stacks generation. Subsequently, the similarity between patch stacks is compared by a sliding time-series likelihood ratio (STSLR) test algorithm in step 2, which utilizes the multi-dimensional data structure of the stacks to improve the accuracy of unchanged pixels detection. Finally, the filtered values are obtained by averaging the similar pixels in time-series. The experimental results and analysis of two multitemporal datasets acquired by TerraSAR-X show that the proposed method outperforms the other typical methods with regard to the overall filtering effect, especially in terms of the consistency between the filtered images and the original ones. Furthermore, the performance of the proposed method is also discussed by analyzing the results from step 1 and step 2.

Understanding Mountain-Wave Phases in ERS Tandem DInSAR Interferogram Using WRF Model Simulation

Repeat-pass spaceborne differential synthetic aperture radar interferometry (DInSAR) is commonly used to measure surface deformation. However, the phase delay due to the atmospheric water vapor has a significant influence on the accuracy of DInSAR results. On the other hand, the signal delay in DInSAR can give the spatial variation information of water vapor during the two image acquisitions. There are some ripple-like phase signals in DInSAR, which are probably due to the mountain wave in the study area. In this paper, the weather research and forecasting (WRF) model is used to simulate the atmospheric delay and compared to the phase delay derived from DInSAR results to understand the mountain-wave phases in interferograms. The results indicate that the ripple-like phase signal in the DInSAR phase is due to different intensities of the mountain wave in two acquisitions. The WRF model can be used to explain the mechanism of mountain-wave phase in DInSAR, which may then, hopefully, be used for DInSAR atmospheric correction.

A novel interferogram quality assessment index based on connected area

InSAR interferogram quality assessment is a key step for the using of interferogram map. Traditionally, the interferogram is qualitatively assessed visually and quantitatively assessed by the number of residues. However, the important structure information is hardly quantifiable. This paper presents a novel index to evaluate the quality of InSAR interferogram based on connected area. After discomposing the fringes into independent connected areas, we analyze the statistical ratio of an area to its margin. Then we use the ratio as an index to quantitatively evaluate the interferogram. In the end, the presented index is used for the filtered interferogram of popular filters, and the results fit the visual judging.

A Novel InSAR phase denoising method via nonlocal wavelet shrinkage

In this paper, an interferometric synthetic aperture radar phase denoising method which utilizes both local sparsity of wavelet coefficients and nonlocal similarity of grouped blocks, has been proposed. The derived nonlocal wavelet shrinkage use double L1 norm restrictions, which enforce local and nonlocal sparsity constraints by efficient shrinkage operators. This method can take advantage of the coefficients of nonlocal similarity between group blocks for wavelet shrinkage, and improve the accuracy of filtering result. Experimental results in InSAR phase image denoising tasks with simulation and actual noise data show that the proposed method outperforms the state of the art with lower root-mean-square error and less noisy fringes, making it possible to effectively filtering phase noise with superior performance.

A phase offset calculation algorithm for airborne repeat-pass interferometry using external DEM

The absolute phase retrieval in interferometry processing of synthetic aperture radar (SAR) requires the calculation of a constant phase offset. This operation is usually carried out by exploiting corner reflectors (CRs) that the geographic information is measured by GPS measurements. However, the deployment and measurement of CRs are difficult in unfriendly area or natural disaster scenarios. However, the phase offset calculation based on external digital elevation model (DEM) is affected by time-varying baseline error in airborne repeat-pass InSAR system. A time-varying baseline estimation method and correction method are incorporated in the phase offset calculation algorithm. The proposed procedure is applied to two repeat-pass InSAR dataset acquired by Chinese Academy of Science (CAS) C-band system.