认知雷达波形自适应数据关联跟踪算法

摘要

For the multiple cross-maneuvering targets tracking in the background of clutter,a waveform self-Adaption data association algorithm for cognitive radar tracking is proposed.This algorithm chooses the range-velocity-bearing as the measurement,and adjusts the waveform parameters to vary the error covariance of the measurement dynamically.Firstly,an optimization probability data association algorithm (OPDA) is given based on the information fusion theory.This algorithm fuses the target position characteristics and motion characteristics to classify the public measurement in the cross area,and makes the multiple cross-maneuvering targets tracking problem into the multiple single-maneuvering target tracking problem.Secondly,the Riccati equation is used to estimate the filtering covariance for the updated target track,and the next waveform is chosen adaptively to improve the tracking performance according to the criterion function of the waveform selection.Simulation results show that this algorithm enhances the environment adaptability of the PDA algorithm,and has superiority than the algorithm without waveform self-adaption.%针对杂波背景下多交叉机动目标跟踪问题,提出一种认知雷达波形自适应数据关联跟踪算法,该算法选取目标距离-速度-方位作为观测量,并通过调整波形参数来动态改变量测误差协方差.首先,基于信息融合思想提出一种优化的概率数据关联(OPDA)算法,算法充分融合目标位置特征和运动特征对多目标交叉区域公共量测进行分类,使多交叉机动目标跟踪问题转化为多个单机动目标跟踪问题.然后,对实时更新的目标航迹,采用修正的Riccati方程估计下一时刻滤波协方差,并根据波形选择准则函数自适应选择下一时刻波形以提高系统跟踪性能.仿真结果表明,该算法增强了概率数据关联(PDA)算法的环境适应性,而且相比未采用波形自适应的数据关联算法有明显的优势.