针对无人机在复杂任务空间执行作战任务过程中的航路规划问题,提出了一种对于快速扩展随机树算法(Rapidly-exploring Random Trees,RRT)的综合改进航路规划算法。为了提高算法的收敛速度,加快生成航路的时间并减少扩展树分支,在传统RRT...针对无人机在复杂任务空间执行作战任务过程中的航路规划问题,提出了一种对于快速扩展随机树算法(Rapidly-exploring Random Trees,RRT)的综合改进航路规划算法。为了提高算法的收敛速度,加快生成航路的时间并减少扩展树分支,在传统RRT算法中加入了引力场引导随机树节点向目标点的扩展。同时针对该算法中固定生长步长下生成随机树冗余节点较多、生成航路曲折的问题,使用了动态生长步长策略对算法进行改进。仿真实验结果表明,与传统RRT算法相比较,综合改进的RRT算法在搜索时间上节省了80%,搜索路径节点个数减少了50%,且在航路平滑性等性能中有明显提升,算法整体性能更优。展开更多
With the sea-level rising,the measurement of sea surface height(SSH) is attracting more and more attention in the area of oceanography.Satellite radar altimeter is usually used to measure the SSH.However,deviation bet...With the sea-level rising,the measurement of sea surface height(SSH) is attracting more and more attention in the area of oceanography.Satellite radar altimeter is usually used to measure the SSH.However,deviation between the measured value and the actual one always exists.Among others,the sea state bias(SSB) is a main reason to cause the deviation.In general,one needs to estimate SSB first to correct the measured SSH.Currently,existing SSB estimation methods more or less have shortcomings,such as with many parameters,high prediction error and long training time.In this paper,we introduce an effective and efficient linear model called LASSO to the SSB estimation.The LASSO algorithm minimizes the residual sum of squares under the condition that the sum of the absolute values of each coefficient is less than a certain constant.In the implementation of LASSO,we use the significant wave height and wind speed to fit the LASSO model.Hence,the applied model has only 3 parameters,corresponding to the two inputs and a bias.Experimental results on the data of JASON-2,JASON-3,T/P and HY-2 radar altimetry show that LASSO performs better than geophysical data records(GDR) and ordinary least squares(OLS) estimator.Moreover,from the running time,we can see that LASSO is very efficient.展开更多
文摘针对无人机在复杂任务空间执行作战任务过程中的航路规划问题,提出了一种对于快速扩展随机树算法(Rapidly-exploring Random Trees,RRT)的综合改进航路规划算法。为了提高算法的收敛速度,加快生成航路的时间并减少扩展树分支,在传统RRT算法中加入了引力场引导随机树节点向目标点的扩展。同时针对该算法中固定生长步长下生成随机树冗余节点较多、生成航路曲折的问题,使用了动态生长步长策略对算法进行改进。仿真实验结果表明,与传统RRT算法相比较,综合改进的RRT算法在搜索时间上节省了80%,搜索路径节点个数减少了50%,且在航路平滑性等性能中有明显提升,算法整体性能更优。
基金supported by the National Key R&D Program of China(No.2016YFC1401004)the Science and Technology Program of Qingdao(No.17-3-3-20-nsh)+1 种基金the CERNET Innovation Project(No.NGII20170416)the Fundamental Research Funds for the Central Universities of China
文摘With the sea-level rising,the measurement of sea surface height(SSH) is attracting more and more attention in the area of oceanography.Satellite radar altimeter is usually used to measure the SSH.However,deviation between the measured value and the actual one always exists.Among others,the sea state bias(SSB) is a main reason to cause the deviation.In general,one needs to estimate SSB first to correct the measured SSH.Currently,existing SSB estimation methods more or less have shortcomings,such as with many parameters,high prediction error and long training time.In this paper,we introduce an effective and efficient linear model called LASSO to the SSB estimation.The LASSO algorithm minimizes the residual sum of squares under the condition that the sum of the absolute values of each coefficient is less than a certain constant.In the implementation of LASSO,we use the significant wave height and wind speed to fit the LASSO model.Hence,the applied model has only 3 parameters,corresponding to the two inputs and a bias.Experimental results on the data of JASON-2,JASON-3,T/P and HY-2 radar altimetry show that LASSO performs better than geophysical data records(GDR) and ordinary least squares(OLS) estimator.Moreover,from the running time,we can see that LASSO is very efficient.
