ID 原文 译文
573 仿真结果表明，所提算法具有良好的抗差性且不受目标运动位置的影响，适用于传感器同地或异地配置等多种情况。 The simulationresults show that the algorithm has good robust performance and is not affected by the target moving position.The algorithm can be applied to the configuration of sensors at same or different sites.
574 近些年来，世界各国越来越重视风力发电的发展。 In recent years, countries around the world have paid more and more attention to the development ofwind power.
575 风电场的存在可能对航管监视雷达性能产生负面影响，因此风电场杂波抑制技术的研究对于提升航管监视雷达工作性能、保障空中交通安全具有重大意义。 The existence of wind farms may have a negative impact on the performance of air traffic controlsurveillance radars. Therefore, the research on the clutter suppression technology of wind farms is of greatsignificance to improve the work performance of air traffic control surveillance radars and ensure the safety ofair traffic.
576 形态成分分析(MCA)算法根据信号稀疏特征的不同应用于风电场杂波抑制时，计算量较低且性能较好。 When the Morphological Component Analysis(MCA)algorithm is applied to the wind farm cluttersuppression based on the difference of sparse characteristics for the signals, the calculation burden is lower andthe performance is better.
577 但是针对实际雷达参数中相参处理间隔(CPI)较短造成的谱分辨率降低及信号特征不明显时，MCA算法的杂波抑制性能受到影响，因此选择将稀疏重构算法与MCA算法结合用于短CPI情况下的风电场杂波抑制。 However, the clutter suppression performance of the MCA algorithm is affected whenthe spectral resolution is reduced due to the short Coherent Processing Interval(CPI)and the signalcharacteristics are not obvious.Therefore, the sparse reconstruction algorithm and the MCA algorithm arecombined to suppress the clutter in the wind farm with a small number of coherent pulses.
578 该文认为短CPI接收回波数据为较长CPI雷达回波数据基础上发生尾部数据缺省，继而利用稀疏重构算法对缺省数据进行恢复，再利用MCA算法抑制风电场杂波。 It is considered thatthe short CPI received echo data is the default of tail data on the basis of the longer CPI radar echo data, andthen the sparse reconstruction algorithm is used to recover the default data, and the MCA algorithm is used tosuppress wind farm clutter.
579 实验结果验证了该方法的有效性。 The experimental results verify the effectiveness of the proposed method.
580 针对低检测概率下多机动目标的跟踪问题，该文提出一种新的交互式多传感器多目标多伯努利滤波器(IMM-MS-MeMBer)。 A novel method Interacting Multiple Mode Multi-Sensor Multi-target Multi-Bernoulli (IMM-MS-MeMBer) filter to track multiple maneuvering targets in low detection probability scenario is proposed.
581 在IMM-MS-MeMBer滤波器的预测阶段，该文利用当前的量测信息自适应地更新目标的模型概率，并利用更新后的模型概率对目标状态进行混合预测；在IMM-MS-MeMBer滤波器的更新阶段，使用贪婪的多传感器量测划分策略对多传感器量测进行划分，并利用得到的量测划分集合和IMM-MS-MeMBer滤波器对目标的后验概率密度进行更新；除此之外，IMM-MS-MeMBer滤波器能够利用目标的角度和多普勒量测信息同时实现多个机动目标的位置、速度估计。 At theprediction stage of the IMM-MS-MeMBer filter, model probability of the target is adaptively updated byutilizing the current measurement information, and then the mixed prediction of the target state is executed;At the update stage of the IMM-MS-MeMBer filter, the greedy multi-sensor measurement partitioning strategyis employed in measurement partition step, the posterior probability density of the target is updated by usingthe divided set of measurements and the IMM-MS-MeMBer filter; In addition, the IMM-MS-MeMBer filterutilizes the target angle and Doppler information to realize the simultaneous estimation of the position andspeed of multiple maneuvering targets.
582 数值实验验证了该文所提IMM-MS-MeMBer滤波器的优越性能。 Numerical experiments verify the superior performance of the IMM-MS-MeMBer filter.