ID 原文 译文
40306 匹配算法优化首先根据支持向量机(Support vector machine，SVM)对待定位点分类，获取其对应的区域编号， The matching algorithm is optimized to classify the points to be located according to the support vector machine(SVM)and obtain the corresponding area ids.
40307 再将欧氏距离、曼哈顿距离和切比雪夫距离三者结合得到位置估计。 The Euclidean distance, Manhattan distance and Chebyshev distance are combined to obtain a position estimate.
40308 最后，结合行人航位推算(Pedestrian dead reckoning，PDR)算法将得到的步长与航向角一同进行粒子滤波(Particle filtering，PF)实现定位。 Finally, combined with the pedestrian dead reckoning(PDR)algorithm, the obtained step size and heading angle are subjected to particle filtering to achieve positioning.
40309 实验表明：本文的算法将定位精度提高了13.92%。 The proposed algorithm improves the positioning accuracy by 13.92%.
40310 模糊函数主脊(Ambiguity function main ridge，AFMR)切面特征能较好地反映不同信号结构上的本质差别，是解决当前复杂体制雷达辐射源信号分选难题的可行参数， The feature of the slice of ambiguity function main ridge can better reflect the structural essential differences between signals, and it is a feasible parameter to solve the current complex system radar emitter signal sorting problem.
40311 而快速、智能地搜索模糊函数主脊切面是增加其切面特征实用性的重要问题。 The fast and intelligent search for the slice of ambiguity function main ridge is an important issue to increase the practicability of its feature of the slice.
40312 为此，本文构建了一种结合均匀初始化策略和改进非线性收敛因子的改进自适应灰狼算法来搜索典型6种雷达辐射源信号的模糊函数主脊切面并提取切面特征，并与穷举法和标准灰狼算法进行对比。 In this paper, an improved selfadaptive grey wolf optimization(GWO)combining uniform initialization strategy and improved nonlinear convergence factor was proposed to search the main ridge slice of ambiguity function of six typical radar emitter signals and extract the feature of slices, which were compared with the exhaustive method and standard GWO.
40313 实验结果表明，所提方法在搜索AFMR切面并提取特征时，平均耗时仅为1.49 s，相较于穷举法和标准灰狼优化算法，效率分别提高了75.7%和19.0%，具有较优的时效性。 The experimental results showed that the average time consumption of the proposed method was only 1.49 s when searching AFMR slice and extracting feature. Compared with the exhaustive method and the standard GWO, the efficiency was improved by 75.7% and 19.0%, respectively, with better timeliness.
40314 在固定信噪比(Signal-to-noise ratio，SNR)环境下，当SNR不低于0 dB时，提取到特征值的平均聚类准确率为96.4%，在0～20 dB动态信噪比环境下，平均聚类准确率可达95.2%，具有较好的准确性、抗噪性能及较强的类内聚集性和类间分离能力，证实了所提方法的可行性与有效性。 In a fixed SNR environment, when the SNR was not less than 0 dB, the average clustering accuracy of the extracted feature values was 96.4%;and in a dynamic SNR environment of 0—20 dB, the average clustering accuracy can reach 95.2%, with good accuracy, anti-noise performance and strong intra-class aggregation and inter-class separation ability, which proves the feasibility and effectiveness of the proposed method.
40315 盲源分离(Blind source separation，BSS)是一种从混合信号中提取和恢复源信号的信号处理方法。 Blind source separation(BSS)algorithm is utilized in extracting and recovering source signals from mixed signals.