ID 原文 译文
21545 主要对周期探测信号调制Chirp相位编码，通过回波慢时间维解码使杂波近似白化，降低杂波与目标回波相关性，再依据白化后杂波及有用信号成分自相关性差异分离出信号和杂波干扰子空间； In the framework, the periodic detectionsignal is modulated with phase encoding, and the clutter is whitened through echo decoding of the slow-time dimension to reduce the correlation between clutter and target echo. Furthermore, interference subspace is constructed on the basis of the autocorrelation differences between whitened clutter and useful signal components.
21546 最后将接收信号投影至正交于杂波子空间的信号子空间来抑制杂波。 The receiving signal is projected to the signal subspace orthogonal to the clutter subspace for clutter suppression.
21547 由于该方法中杂波空间的构建不需要假设杂波模型，避免了模型假设与实际环境不匹配的问题。 Since the construction of clutter space does not need to assume the clutter model, it avoids the problem of mismatch between the model hypothesis and the actual environment.
21548 仿真结果和实测数据处理结果证明该方法在低信杂比条件下性能明显优于传统方法。 Simulation results and real data processing results show that this method has better performance than conventional methods under lowsignal-to-clutter ratio.
21549 针对目标个数未知时双基地MIMO雷达角度跟踪问题，该文提出一种基于改进自适应非对称联合对角化(AAJD)的目标个数与角度联合跟踪算法。 In order to solve the angle tracking problem of bistatic MIMO radar when the number of target is unknown, a joint tracking algorithm of the number of target and the angle is proposed.
21550 AAJD算法中无法得到特征值变量，因此改进AAJD算法引入主成分顺序估计思想，循环求出特征值，然后运用改进信息论准则估计出目标个数。 There is no variable in Adaptive Asymmetric Joint Diagonalization (AAJD) algorithm that can directly represent the eigenvalue. Therefore, the idea of principal component sequence estimation is introduced to the improved AAJD algorithm, and the eigenvalues are iteratively evaluated. Then, the number of target is estimated by using the improved information theory.
21551 其次提出目标个数防抖动算法，提高了稳健性。 Secondly, the anti-dithering algorithm of target number is proposed, which improves the robustness of the algorithm.
21552 最后改进了ESPRIT算法，完成了目标参数的自动配对和关联。 Finally, the ESPRIT algorithm is improved to realize the automatic matching and association of DOD and DOA.
21553 仿真结果表明改进AAJD算法能够成功跟踪目标个数和角度，验证了理论分析的有效性。 The simulation results show that the improved AAJD algorithm can successfully track the number of target and angle trajectories. The efficiency of the proposed method is verified.
21554 该文受人脑视觉感知机理启发，在深度学习框架下提出融合时空双网络流和视觉注意的行为识别方法。 Inspired by the mechanism of human brain visual perception, an action recognition approach integrating dual spatio-temporal network flow and visual attention is proposed in a deep learning framework.