ID	原文	译文
2683	另外，设计高斯粒子的联合采样方法对多目标滤波器的预测高斯分量进行采样，用一组带权值的粒子去近似多目标统计特性，利用理想量测集对粒子的权值进行更新，	Then, taking advantage of this information distance, this paper proposes a corresponding sensor control strategy. Furthermore, ajoint sampling method of Gaussian particle is designed to sample the predicted Gaussian component of multi-target filter. Subsequently, a set of weighted particles are used to approximate the multi-target statistical characteristic, and their weightsare updated with the ideal measurement set.
2684	继而研究利用 Rényi 散度作为评价函数，提出一种适应性更好的传感器控制策略。	Next, a Rényi divergence based sensor control strategy which has better adapta-bility is proposed.
2685	最后，给出基于目标势的后验期望(Posterior Expected Number of Targets，PENT)评价的高斯混合实现过程。	Finally, a detailed Gaussian mixture implementation of the posterior expected number of targets (PENT)is given.
2686	仿真实验验证了提出算法的有效性。	Simulation results verify the effectiveness of these algorithms.
2687	针对机动目标的跟踪问题，提出一种结合自适应匀速(Constant Acceleration，CA)模型和波形调度的平方根容积卡尔曼滤波(Square-Root Cubature Kalman Filter，SCKF)算法。	Aiming at the maneuvering target tracking problem, a novel square-root cubature Kalman filter (SCKF)is proposed by the integration of the adaptive constant acceleration (CA)model and the waveform scheduling.
2688	在 CA 模型的基础上，通过构建 Jerk 分量与速度、加速度的近似关系，使得状态过程噪声与滤波器输出的状态协方差矩阵相联系，以实现模型的自适应调整。	On the basis of the CA model, the approximation relationship between the Jerk and the velocity as well as the acceleration is established inorder to make the connection of the state process noise with the state error covariance matrix.
2689	另外，利用分数阶傅里叶变换(Fractional Fourier Transform，FrFT)旋转发射波形的模糊函数，使得量测误差椭圆与滤波算法中的状态预测误差椭圆正交，	As such, the adaptive adjust-ment of the proposed model is realized. Additionally, the fractional Fourier transform (FrFT)is utilized to rotate the ambi-guity function of the transmitted waveform to maintain the orthogonality between the measurement error ellipse and the state prediction error ellipse.
2690	得到最优的发射波形，以从数据处理和信号处理两方面共同提升系统的跟踪性能。	Thereby, the optimal transmitting waveform can be obtained and the tracking performance is system-atically improved in both of the data processing and the signal processing.
2691	仿真结果表明，相比于基于改进当前统计(current statistical，CS)模型的无迹卡尔曼滤波(Unscented Kalman Filter，UKF)算法、基于 CS 模型的 SCKF 算法、基于 CA 模型的 SCKF 算法和交互式多模型(IMM)SCKF 算法，所提算法结构简单且跟踪精度更高。	The simulation results show that the proposed al-gorithm possess a simpler structure and higher accuracy than the unscented Kalman filter based on the modified current sta-tistical (CS)model, the SCKF based on the CS model, the SCKF based on the CA model and the interactive multiple model SCKF (IMM-SCKF).
2692	多普勒雷达目标跟踪中，如何有效解决系统量测与目标状态之间的非线性并实现机动目标跟踪，是亟待解决的问题。	In the target tracking of Doppler radar, how to effectively solve the nonlinear relationship between the measurement of the system and the target's state and achieve maneuvering target tracking on this basis is an urgent prob-lem.