针对水下目标被动定位中传感器位置误差带来的定位精度不高的问题,提出了一种基于两步最小二乘的到达时间差波达方向(time difference of arrival-direction of arrival,TDOA-DOA)目标定位算法。首先,构建TDOA-DOA理想化无误差模型,并...针对水下目标被动定位中传感器位置误差带来的定位精度不高的问题,提出了一种基于两步最小二乘的到达时间差波达方向(time difference of arrival-direction of arrival,TDOA-DOA)目标定位算法。首先,构建TDOA-DOA理想化无误差模型,并利用最小二乘算法对目标位置进行粗估计。其次,考虑测量误差和传感器位置误差,构建目标定位误差和传感器位置的联合方程,并利用加权最小二乘求解。最后,利用目标定位误差对目标位置粗估计值进行修正,得到更精确的定位结果。仿真实验表明,所提算法可对目标位置和传感器位置进行联合估计,相较于已有算法具有更高的定位精度,更适用于传感器位置存在误差情况下的水下目标定位。展开更多
An efficient solution for locating a target was proposed, which by using time difference of arrival （TDOA） measurements in the presence of random sensor position errors to increase the accuracy of estimation. The ca...An efficient solution for locating a target was proposed, which by using time difference of arrival （TDOA） measurements in the presence of random sensor position errors to increase the accuracy of estimation. The cause of position estimation errors in two-stage weighted least squares （TSWLS） method is analyzed to develop a simple and effective method for improving the localization performance. Specifically, the reference sensor is selected again and the coordinate system is rotated according to preliminary estimated target position by using TSWLS method, and the final position estimation of the target is obtained by using weighted least squares （WLS）. The proposed approach exhibits a closed-form and is as efficient as TSWLS method. Simulation results show that the proposed approach yields low estimation bias and improved robustness with increasing sensor position errors and thus can easily achieve the Cramer-Rao lower bound （CRLB） easily and effectively improve the localization accuracy.展开更多
Using machine vision to identify and sort scattered regular targets is an urgent problem to be solved in automated production lines.This study proposed a three-dimensional(3D)recognition method combining monocular vis...Using machine vision to identify and sort scattered regular targets is an urgent problem to be solved in automated production lines.This study proposed a three-dimensional(3D)recognition method combining monocular vision and machine learning algorithms.According to the color characteristics of the targets,to convert the original color picture into YCbCr mode and use the 2D Otsu algorithm to perform gray level image segmentation on the Cb channel.Then the Haar-feature training was carried out.The comparison of feature training and Haar method for Hough transform showed that the recognized time of Haar-feature AdaBoost trainer reached 31.00 ms,while its false recognized rate was 3.91%.The strong classifier was formed by weight combination,and the Hough contour transformation algorithm was set to correct the normal vector between plane coordinate and camera coordinate system.The monocular vision system ensured that the field of camera view had not obstructed while the dots were being struck.It was measured and calculated angles between targets and the horizontal plane which coordinate points of the identified plane feature.The testing results were compared with the Otsu and AdaBoost trainer where the prediction and training set have an error of no more than 0.25 mm.Its correct rate can reach 95%.It shows that the Otsu and Haar-feature based on AdaBoost algorithm is feasible within a certain error ranges and meet the engineering requirements for solving the poses of automated regular three-dimensional targets.展开更多
文摘针对水下目标被动定位中传感器位置误差带来的定位精度不高的问题,提出了一种基于两步最小二乘的到达时间差波达方向(time difference of arrival-direction of arrival,TDOA-DOA)目标定位算法。首先,构建TDOA-DOA理想化无误差模型,并利用最小二乘算法对目标位置进行粗估计。其次,考虑测量误差和传感器位置误差,构建目标定位误差和传感器位置的联合方程,并利用加权最小二乘求解。最后,利用目标定位误差对目标位置粗估计值进行修正,得到更精确的定位结果。仿真实验表明,所提算法可对目标位置和传感器位置进行联合估计,相较于已有算法具有更高的定位精度,更适用于传感器位置存在误差情况下的水下目标定位。
基金supported by the National Natural Science Foundation of China (61271236, 61601245)the Open Research Program of the State Key Laboratory of Millimeter Waves (K201724)the China Postdoctoral Science Foundation Funded Project (2016M601693)
文摘An efficient solution for locating a target was proposed, which by using time difference of arrival （TDOA） measurements in the presence of random sensor position errors to increase the accuracy of estimation. The cause of position estimation errors in two-stage weighted least squares （TSWLS） method is analyzed to develop a simple and effective method for improving the localization performance. Specifically, the reference sensor is selected again and the coordinate system is rotated according to preliminary estimated target position by using TSWLS method, and the final position estimation of the target is obtained by using weighted least squares （WLS）. The proposed approach exhibits a closed-form and is as efficient as TSWLS method. Simulation results show that the proposed approach yields low estimation bias and improved robustness with increasing sensor position errors and thus can easily achieve the Cramer-Rao lower bound （CRLB） easily and effectively improve the localization accuracy.
基金This work was financially supported by the National Natural Science Foundation of China(Grant No.51705365)The authors also acknowledge the State Key Research Program of China(Grant No.2017YFD0700404)+1 种基金the Guangdong Provincial Department of Education Project(Grant No.2016KZDXM027)the Guangdong Provincial Department of Agriculture(Grant No.2019KJ129).
文摘Using machine vision to identify and sort scattered regular targets is an urgent problem to be solved in automated production lines.This study proposed a three-dimensional(3D)recognition method combining monocular vision and machine learning algorithms.According to the color characteristics of the targets,to convert the original color picture into YCbCr mode and use the 2D Otsu algorithm to perform gray level image segmentation on the Cb channel.Then the Haar-feature training was carried out.The comparison of feature training and Haar method for Hough transform showed that the recognized time of Haar-feature AdaBoost trainer reached 31.00 ms,while its false recognized rate was 3.91%.The strong classifier was formed by weight combination,and the Hough contour transformation algorithm was set to correct the normal vector between plane coordinate and camera coordinate system.The monocular vision system ensured that the field of camera view had not obstructed while the dots were being struck.It was measured and calculated angles between targets and the horizontal plane which coordinate points of the identified plane feature.The testing results were compared with the Otsu and AdaBoost trainer where the prediction and training set have an error of no more than 0.25 mm.Its correct rate can reach 95%.It shows that the Otsu and Haar-feature based on AdaBoost algorithm is feasible within a certain error ranges and meet the engineering requirements for solving the poses of automated regular three-dimensional targets.
