ID 原文 译文
50347 针对这一问题，首先分析了进动锥体弹头和摆动锥体诱饵的微多普勒形式，得到了同一目标不同散射点的微多普勒具有相同的周期性。 In order to solve this problem, firstly analyzes the precession cone warhead and rocking bait form of micro-doppler, got the same target different scattering point of micro-doppler with the same periodicity.
50348 对于多目标分离问题，首先利用Radon变换估计平动参数实现了多目标平动补偿; To the problem of multi-objective separation first Radon transform is used to estimate the translational parameters to realize the multi-objective translational compensation;
50349 之后通过分析多目标时频图循环平稳性，发现弹道多目标分离本质上是多个二维一阶循环平稳(first-order cyclostationary，FOCS)分量的分离问题; By analyzing multi-objective time-frequency diagram after cycle stability, found that multi-objective separation is essentially multiple ballistic two-dimensional first-order loop smoothly (first - order cyclostationary, FOCS) component separation problems;
50350 其次，提出了一种基于二维FOCS处理的多目标分离方法; Secondly, this paper proposes a multi-objective separation method based on two-dimensional FOCS processing;
50351 最后，通过仿真验证了该方法的有效性和在强噪声下的稳定性。 Finally, the effectiveness of the proposed method is validated by computer simulation and stability under the condition of strong noise.
50352 针对传感器网络(sensor network，SN)目标融合检测应用中融合中心无法精确地获得局部传感器节点检测性能参数的问题，建立了基于SN的目标融合检测系统，提出了一种非理想信道条件下在线决策融合的目标检测方法。 (in wireless sensor network sensor network, SN) target fusion detection application in fusion center cannot accurately obtain local sensor node detection performance parameters of the problem, based on SN target fusion detection system, puts forward a kind of ideal channel conditions online method for target detection decision fusion.
50353 该方法依据解调后数据构建了节点未知虚警概率、检测概率以及节点与融合中心信道平均传输错误概率等未知参数求解模型，并采用非线性最小二乘方法在线地估计出这些未知参数。 The method is based on the data to construct a node after demodulation false-alarm probability and detection probability and the unknown node and the fusion center channel average transmission error probability of the unknown parameters to solve the model, and USES the nonlinear least squares method to estimate the unknown parameters online.
50354 进而通过选择性能优的节点参与融合，最大化融合检测系统检测概率。 Then by selecting the optimal fusion nodes involved in performance, maximize fusion detection system detection probability.
50355 仿真结果表明:这种在线决策融合方法能够准确地估计出传感器节点的概率参数以及信道的平均传输错误率; The simulation results show that the online decision fusion method can accurately estimate the probability parameters of the sensor node and the average transmission channel error rate;
50356 相比于已知先验的最优似然比融合规则，在线决策融合规则检测性能相当。 Compared with the known a priori optimal likelihood ratio fusion rules, online detection performance decision fusion rules.