ID 原文 译文
7294 针对突发威胁,无人机重新规划局部航迹的问题,提出了分段优化快速扩展随机树(rapidly-exploring random tree,RRT)的无人机动态航迹规划算法。 For emergent threats, unmanned aerial vehicle (uav) to planning and local path problem, rapidly expanding section optimization random tree (rapidly - exploring the random tree, RRT) uav dynamic path planning algorithm.
7295 首先利用分段优化RRT算法生成全局航迹; Global path generated by piecewise optimized RRT algorithm first;
7296 然后根据突发威胁的相关信息确定局部航迹的起点和终点; And then determined according to relevant information of the unexpected threat to local track is the starting point and end point;
7297 最后利用分段优化RRT算法生成局部航迹,绕过突发威胁并回到原航迹。 Finally local path generated by piecewise optimized RRT algorithm, bypass the emergent threats and return to the original track.
7298 实验结果表明算法运行时间和路径代价都降低了10%左右。 The experimental results show that the algorithm running time and the path cost was reduced by 10% or so.
7299 对于动态航迹规划,该算法的鲁棒性与实时性较强。 For dynamic path planning, the robustness of the algorithm and the real-time performance is stronger.
7300 预警雷达抗噪声压制干扰评估通常需要对量化后的瑞利噪声样本进行估计。 Early warning radar noise suppressing interference assessment usually need to quantify the Rayleigh noise after samples are estimated.
7301 在实际雷达系统中受硬件采样位数限制有时会舍弃噪声样本的低位数据,相当于进一步提高了噪声的量化误差,这时采用截断后的噪声样本计算均值会造成很大的估计偏差。 In the practical radar system restricted by hardware sampling digit sometimes give up the low noise sample data, is to further improve the quantization error of noise, after then USES the truncation of noise samples calculated mean could do a lot of bias.
7302 为了有效运用低位截断后的噪声样本进行噪声估计,基于多项分布和瑞利分布推导了分布参数的最大似然估计方法,并证明了对数似然函数的凸函数性质以及后验分布为对数凹分布的性质,在此基础上提出了无信息先验贝叶斯估计方法和共轭先验贝叶斯估计方法。 In order to effectively use low noise after truncation noise estimation samples, based on the multinomial distribution and Rayleigh distribution were derived distribution parameter of the maximum likelihood estimation method, and proves that the logarithmic likelihood function of the nature of the convex function and the nature of the posterior distribution for the logarithmic concave distribution, based on the presented information prior bayesian estimation method and the conjugate prior bayesian estimation method.
7303 仿真数据实验验证了所提的极大似然估计、无信息先验贝叶斯估计和共轭先验贝叶斯估计算法的有效性,且对比分析了贝叶斯估计相对于极大似然估计的优越性。 Simulation experiments verify the effectiveness of the proposed data maximum likelihood estimation, without a priori information bayesian estimation and the effectiveness of the conjugate prior bayesian estimation algorithm, and the contrast analysis of the bayesian estimation compared with the advantages of maximum likelihood estimation.