4D毫米波雷达稀疏点云下的连续帧隐空间法线估计

针对毫米波连续调频雷达在室外运动场景下点云数据稀疏和空间配准精度低的问题,本文提出了一种轻量级空间配准方法。该方法适用于室外运动场景下毫米波雷达连续多帧之间的点云处理,能在没有位姿传感器辅助的情况下,通过时空图神经网络准确估计相邻多帧点云的隐空间法线,并将每帧雷达点云数据转换到统一的观测坐标系中,从而实现四维点云的多帧融合与场景配准。实验结果表明,该方法不仅能准确评估四维点云的空间姿态,还能有效校正和融合每帧点云的坐标,在运动及震动过程中实现点云坐标的精准配准。此外,该算法还能显著提高点云成像的密度,增强图像的精度和可读性,同时适用于静态和动态目标的成像,为毫米波雷达在室外运动场景的应用提供了有力支持。

基于法线估计的鼢鼠头骨三维点云分割算法

针对复杂背景分割时出现的低效率、过分割等问题,提出一种基于法线估计的三维点云分割方法。首先,改进随机采样一致性(RANSAC)算法,引入法线约束去除背景中大部分平面点云,使得目标点云与大量点云分离,然后采用欧式聚类分割从中提取目标点云。利用鼢鼠头骨三维点云数据进行试验,结果表明,融合的点云分割算法可以将目标鼢鼠头骨点云精确分割,完全去除冗余点云,速度快、准确率高,相较同类型方法表现更优。

基于改进3RDP算法的法线估计散乱点云轮廓提取

为解决通过法线估计利用角度阈值判断点云轮廓时提取的点云轮廓线不完整和不准确的问题,提出基于改进的三维道格拉斯-普克(3RDP)算法,并对法线估计法进行了优化。首先利用传统的法线估计方法在低阈值下获取轮廓特征候选点,通过3RDP算法对候选点进行抽稀,并对算法进行了改进。然后提出了利用主成分分析选取点集的基面,在主方向中找到原点和终点的方法,用以最小距离选取方式将点集进行排序。最后根据点到基面的距离判断点是否属于轮廓线上的点,同时去除内部点,提取目标物轮廓特征。实验证明了该算法可以很好地剔除法线估计方法中不属于轮廓线上的点,与传统法线估计算法相比,提取出的目标物轮廓线更加完整和准确。

改进法线方向的点云实时分割提取平面方法研究

为了提高服务机器人在室内环境中目标物体抓取的效率,提出一种针对三维点云数据改进表面法线估计的方法;重点研究了如何提高使用三维点云数据实时性和准确性,这是机器人抓取过程的关键因素;使用消费级别的传感器kinectV2获取点云数据,提出使用积分图像降低滤波区域内像素的遍历消耗的时间,从而提高实时性;采用动态调整平滑区域的大小以及分配权重的方法平滑处理点云数据的噪声,提高法线估计精度;依据改进后的法线方向,利用区域生长算法对不同指向的法线进行分类,然后对点云数据实施分割提取平面;最后,采用提出的方法和点云库中的相关算法,进行了平面分割提取测试比较;结果表明,利用kinectV2获取的深度数据(1-5米)比目前点云库中的平面分割算法数据精度更高、实时性更强。

基于深度学习的平面位姿估计算法

为了在未知物体三维模型的情况下使用深度学习进行平面位姿估计,采用编码器-解码器网络,从单个RGB图像中检测平面实例分割及法线信息,并利用这些信息进行位姿解算,获得每个平面的实时位姿。实验结果显示,平面召回率为0.625,平面法线召回率为0.414,实时性为18.5 f/s,验证了算法的可行性。

基于卷积神经网络的航空零件去噪技术

为去除在用三维激光扫描技术扫描航空零部件时,因扫描环境、设备等因素带来的大量零件点云噪声,提出基于卷积神经网络的航空零件去噪技术。首先应用经典卷积神经网络预测点云法线信息,然后以此进一步对点云进行位置更新,从而实现点云去噪。经实验证明,与目前的去噪方法相比,所提方法在去噪方面更具优越性。

Free Search Algorithm Based Estimation in WSN Location

This paper proposes a novel intelligent estimation algorithm in Wireless Sensor Network nodes location based on Free Search,which converts parameter estimation to on-line optimization of nonlinear function and estimates the coordinates of senor nodes using the Free Search optimization.Compared to the least-squares estimation algorithms,the localization accuracy has been increased significantly,which has been verified by the simulation results.

A NOVEL SUPER-RESOLUTION BEAMFORMING ALGORITHM

A novel simply-structured hybrid smart antenna system suitable to be used in ad-hoc network terminals is proposed in this letter. The super-resolution beamforming algorithm is also pre-sented based on the system using DOA estimation results. The algorithm can switch the beamforming to the direction of the expected signal and get the best transmitting performance after the pre-beamforming of the Butler matrix. The shifting value formulas are presented to obtain the best SNR when there is no interfering signal and to acquire the highest Signal to Interference Ratio (SIR) as there is one interfering signal. When there are more than one interfering signals,the pre-beamforming feature of the Butler matrix can also suppress the interfering signals. Simulation results verified the algorithm.

Estimating the Predictability of the Quasi-Biweekly Oscillation Using the Nonlinear Local Lyapunov Exponent Approach

The quasi-biweekly oscillation (QBWO) is a major intraseasonal variability (ISV) in the tropics. Based on bandpass-filtered outgoing longwave radiation (OLR) and wind field data, the predictability limits of the QBWO in boreal summer and boreal winter are investigated using the nonlinear local Lyapunov exponent (NLLE) approach The analysis shows that the evolution of the mean error growth of the QBWO in boreal summer and the evolution of the mean error growth in boreal winter are comparable Both curves exhibit rapid growth in the initial stage followed by a slowly fluctuating, ascending trend before saturation is reached. As a result, the potential predictability limits for the boreal summer QBWO are very close to those for the boreal winter QBWO, with a lead time of approximately three weeks. Given the current limitations in the simulation and prediction of ISV, including the QBWO, the results of this study provide a useful reference for assessing the predictability of the QBWO using model simulations.

Design of motion control of dam safety inspection underwater vehicle

Plenty of dams in China are in danger while there are few effective methods for underwater dam inspections of hidden problems such as conduits,cracks and inanitions.The dam safety inspection remotely operated vehicle(DSIROV) is designed to solve these problems which can be equipped with many advanced sensors such as acoustical,optical and electrical sensors for underwater dam inspection.A least-square parameter estimation method is utilized to estimate the hydrodynamic coefficients of DSIROV,and a four degree-of-freedom(DOF) simulation system is constructed.The architecture of DSIROV's motion control system is introduced,which includes hardware and software structures.The hardware based on PC104 BUS,uses AMD ELAN520 as the controller's embedded CPU and all control modules work in VxWorks real-time operating system.Information flow of the motion system of DSIROV,automatic control of dam scanning and dead-reckoning algorithm for navigation are also discussed.The reliability of DSIROV's control system can be verified and the control system can fulfill the motion control mission because embankment checking can be demonstrated by the lake trials.

Improved DOA Estimation Algorithm with Sensor Array Perturbations for CDMA System

An improved direction of arrival (DOA) estimation algorithm with sensor gain and phase uncertainties for synchronous code division multiple access(CDMA) system with decorrelator is presented. Through decorrelating processing DOAs of the desired users can be estimated independently and all other resolved signal interferences are eliminated. Emphasis is directed to applications in which sensor gain and phase are perturbed that often happen actually. It is shown that improved DOA estimation can be achieved for decoupled signals by gain and phase pre estimation procedures.

A Homogeneous Linear Estimation Method for System Error in Data Assimilation

In this paper, a new bias estimation method is proposed and applied in a regional ensemble Kalman filter （EnKF） based on the Weather Research and Forecasting （WRF） Model. The method is based on a homogeneous linear bias model, and the model bias is estimated using statistics at each assimilation cycle, which is different from the state augmentation methods proposed in pre- vious literatures. The new method provides a good estimation for the model bias of some specific variables, such as sea level pres- sure （SLP）. A series of numerical experiments with EnKF are performed to examine the new method under a severe weather condi- tion. Results show the positive effect of the method on the forecasting of circulation pattern and meso-scale systems, and the reduc- tion of analysis errors. The background error covarianee structures of surface variables and the effects of model system bias on EnKF are also studied under the error covariance structures and a new concept ＇correlation scale＇ is introduced. However, the new method needs further evaluation with more cases of assimilation.

Gross Error Detection and Identification Based on Parameter Estimation for Dynamic Systems

The detection and identification of gross errors, especially measurement bias, plays a vital role in data reconciliation for nonlinear dynamic systems. Although parameter estimation method has been proved to be a pow-erful tool for bias identification, without a reliable and efficient bias detection strategy, the method is limited in ef-ficiency and cannot be applied widely. In this paper, a new bias detection strategy is constructed to detect the pres-ence of measurement bias and its occurrence time. With the help of this strategy, the number of parameters to be es-timated is greatly reduced, and sequential detections and iterations are also avoided. In addition, the number of de-cision variables of the optimization model is reduced, through which the influence of the parameters estimated is reduced. By incorporating the strategy into the parameter estimation model, a new methodology named IPEBD (Improved Parameter Estimation method with Bias Detection strategy) is constructed. Simulation studies on a con-tinuous stirred tank reactor (CSTR) and the Tennessee Eastman (TE) problem show that IPEBD is efficient for eliminating random errors, measurement biases and outliers contained in dynamic process data.

Real time remaining useful life prediction based on nonlinear Wiener based degradation processes with measurement errors

Real time remaining useful life(RUL) prediction based on condition monitoring is an essential part in condition based maintenance(CBM). In the current methods about the real time RUL prediction of the nonlinear degradation process, the measurement error is not considered and forecasting uncertainty is large. Therefore, an approximate analytical RUL distribution in a closed-form of a nonlinear Wiener based degradation process with measurement errors was proposed. The maximum likelihood estimation approach was used to estimate the unknown fixed parameters in the proposed model. When the newly observed data are available, the random parameter is updated by the Bayesian method to make the estimation adapt to the item's individual characteristic and reduce the uncertainty of the estimation. The simulation results show that considering measurement errors in the degradation process can significantly improve the accuracy of real time RUL prediction.

Direction of Arrivals Estimation for Correlated Broadband Radio Signals by MVDR Algorithm Using Wavelet

A theoretical relationship between the wavelet transform and the fast fourier transformation(FFT) methods in broadband wireless signal is proposed for solving the direction of arrivals(DOAs) estimation problem. This leads naturally to the derivation of minimum variance distortionless response(MVDR) algorithm, which combines the benefits of subspace methods with those of wavelet, and spatially smoothed versions are utilized which exhibits good performance against correlated signals. We test the method's performance by simulating and comparing the performance of proposed algorithm, FFT MVDR and MVDR with correlated signals, and an improved performance is obtained.

Analysis and evaluation of band-limited ray tracing method

From the analysis and the contrast of band-limited ray tracing method based on Kirchhoff integral, it was found that the method performs many approximations in the derivation process and omits the derivative term of the phase factor in the Kirchhoff integral. Numerical calculations and analysis show that the omission of cor-relation terms in Kirchhoff integrals has a non-negligible effect on the calculation results, and the different terms in the Kirchhoff integral have different effects on different frequency ranges. The method can be applied to ve-locity models containing complex interfaces without changing waves and continuous media. the velocity model and can be extended to elastic waves and continuous media.

Filtering algorithm and direction identification in relative position estimation based on induction loop-cable

A filtering algorithm and direction identification method are presented for the positioning system of the mid-speed maglev train. Considering the special structure of the mid-speed maglev train, the ground position estimation method is adopted for its traction system. As the train is running, the induction loop-cable receives the signal sent by the on-board antenna to detect the position and direction of the train. But the height of the on-board antenna relative to the loop-cable is highly vulnerable to the change of the suspension height and the magnetic field produced by the traction during traveling, which may lead to amplitude fluctuation of the received signal. Consequently, the position estimation may be inaccurate. Therefore, a discrete second-order nonlinear trackdifferentiator is proposed based on the boundary characteristic curves, and the new differentiator could also extract the running direction of the train for the traction system. The experimental results show that the tracking differentiator can effectively filter out the signal interference and can provide accurate direction signal.

LMMSE-based SAGE channel estimation and data detection joint algorithm for MIMO-OFDM system

A new channel estimation and data detection joint algorithm is proposed for multi-input multi-output （MIMO） - orthogonal frequency division multiplexing （OFDM） system using linear minimum mean square error （LMMSE）- based space-alternating generalized expectation-maximization （SAGE） algorithm. In the proposed algorithm, every sub-frame of the MIMO-OFDM system is divided into some OFDM sub-blocks and the LMMSE-based SAGE algorithm in each sub-block is used. At the head of each sub-flame, we insert training symbols which are used in the initial estimation at the beginning. Channel estimation of the previous sub-block is applied to the initial estimation in the current sub-block by the maximum-likelihood （ML） detection to update channel estimatjon and data detection by iteration until converge. Then all the sub-blocks can be finished in turn. Simulation results show that the proposed algorithm can improve the bit error rate （BER） performance.

A novel robust approach for SLAM of mobile robot

The task of simultaneous localization and mapping （SLAM） is to build environmental map and locate the position of mobile robot at the same time. FastSLAM 2.0 is one of powerful techniques to solve the SLAM problem. However, there are two obvious limitations in FastSLAM 2.0, one is the linear approximations of nonlinear functions which would cause the filter inconsistent and the other is the ＂particle depletion＂ phenomenon. A kind of PSO ＆ Hjj-based FastSLAM 2.0 algorithm is proposed. For maintaining the estimation accuracy, H~ filter is used instead of EKF for overcoming the inaccuracy caused by the linear approximations of nonlinear functions. The unreasonable proposal distribution of particle greatly influences the pose state estimation of robot. A new sampling strategy based on PSO （particle swarm optimization） is presented to solve the ＂particle depletion＂ phenomenon and improve the accuracy of pose state estimation. The proposed approach overcomes the obvious drawbacks of standard FastSLAM 2.0 algorithm and enhances the robustness and efficiency in the parts of consistency of filter and accuracy of state estimation in SLAM. Simulation results demonstrate the superiority of the proposed approach.

An Adaptive Finite Element Method Based on Optimal Error Estimates for Linear Elliptic Problems

The subject of the work is to propose a series of papers about adaptive finite element methods based on optimal error control estimate. This paper is the third part in a series of papers on adaptive finite element methods based on optimal error estimates for linear elliptic problems on the concave corner domains. In the preceding two papers (part 1:Adaptive finite element method based on optimal error estimate for linear elliptic problems on concave corner domain; part 2:Adaptive finite element method based on optimal error estimate for linear elliptic problems on nonconvex polygonal domains), we presented adaptive finite element methods based on the energy norm and the maximum norm. In this paper, an important result is presented and analyzed. The algorithm for error control in the energy norm and maximum norm in part 1 and part 2 in this series of papers is based on this result.