ID 原文 译文
51067 针对现代战场中目标往往采用机动方式运动的情况，为了提高目标跟踪的准确性和精确性，结合多传感器数据融合的优点，提出了一种基于波形捷变的多传感器机动目标跟踪方法。 Goals tend to use technology in the modern battlefield maneuver way movement, in order to improve the accuracy and the accuracy of target tracking, combined with the advantages of multi-sensor data fusion, this paper proposes a multiple sensor based on waveform agility maneuver target tracking method.
51068 该算法通过波形捷变来改变量测的精度。 The algorithm through waveform agility to alter the accuracy of measurement.
51069 首先在现有文献的基础上，将波形捷变方式推广到二维空间，把雷达量测的克拉美罗下限(CramerRao lower bound，CRLB)近似为量测误差协方差， First of all, on the basis of the existing literature, will wave agile method is generalized to two-dimensional space, the radar measurements of Latin America, lower limit (CramerRao lower bound, the CRLB) approximation for the measurement error covariance,
51070 由于该CRLB是关于发射波形参量的，从而把雷达跟踪的信号处理与数据处理结合在一起， because the CRLB is about the launch of waveform parameters, thus the tracking radar signal processing and data processing together,
51071 通过波形参量的动态选择得到量测误差协方差的最小值。 through dynamic waveform parameters selection to get the minimum value measurement error covariance.
51072 从而在整个雷达跟踪过程中提高信噪比(signal to noise ratio，SNR)，降低量测误差。 To improve the signal-to-noise ratio in the whole process of radar tracking (signal to noise thewire, SNR) and reduce the measurement error.
51073 其次，在数据处理上，采用多传感器数据融合及粒子滤波进一步提高机动目标跟踪的精度。 Secondly, on the data processing, using multi-sensor data fusion and particle filter to further improve the accuracy of maneuvering target tracking.
51074 最后，将该算法与传统的Kalman滤波、粒子滤波及只对一维空间的量测采用波形捷变的算法和交互多模型方法(interacting multiple model，IMM)进行仿真比较， Finally, the algorithm with the traditional Kalman filter and particle filter and on only one dimension measurement using the algorithm of waveform agility and interacting multiple model method (interacting multiple models, the IMM) are compared,
51075 仿真结果显示该算法对机动目标的跟踪精度显著提高。 and the simulation results show that the algorithm for maneuvering target tracking accuracy improved significantly.
51076 为了弥补阵列天线导向矢量失配和相位测量噪声对测向性能的影响，提出基于方向图拟合与稳健Capon波束形成技术(robust Capon beamforming，RCB)的双向迭代矢量相关测向方法。 To make up for the antenna array steering vector mismatch and phase measurement noise effect on the performance of direction finding, based on pattern matching is proposed and robust Capon beamforming (robust Capon beamforming, RCB) bi-directional iteration vector direction finding method.