相比于传统雷达系统,感知雷达能够通过对复杂多变的电磁环境感知自适应调整雷达发射波形,以适应当前的环境,实现推理和判决的优化,从而获得系统性能的大幅提升。从感知雷达的思想出发,研究了杂波环境下的波形自适应选择问题,提出了一种...相比于传统雷达系统,感知雷达能够通过对复杂多变的电磁环境感知自适应调整雷达发射波形,以适应当前的环境,实现推理和判决的优化,从而获得系统性能的大幅提升。从感知雷达的思想出发,研究了杂波环境下的波形自适应选择问题,提出了一种基于修正概率数据关联(modified probabilistic data association,MPDA)的波形自适应选择目标跟踪算法。采用MPDA算法建立和更新杂波下目标的航迹,利用修正的Riccati方程估计下一时刻的滤波协方差矩阵,并推导了相应的波形优化准则函数。仿真表明,该算法降低了密集杂波条件下的滤波误差,相比于未采用波形优化的PDA和MPDA算法,显著提高了跟踪性能。展开更多
The joint optimization of detection threshold and waveform parameters for target tracking which comes from the idea of cognitive radar is investigated for the modified probabilistic data association(MPDA)filter.The tr...The joint optimization of detection threshold and waveform parameters for target tracking which comes from the idea of cognitive radar is investigated for the modified probabilistic data association(MPDA)filter.The transmitted waveforms and detection threshold are adaptively selected to enhance the tracking performance.The modified Riccati equation is adopted to predict the error covariance which is used as the criterion function,while the optimization problem is solved through the genetic algorithm(GA).The detection probability,false alarm probability and measurement noise covariance are all considered together,which significantly improves the tracking performance of the joint detection and tracking system.Simulation results show that the proposed adaptive waveform-detection threshold joint optimization method outperforms the adaptive threshold method and the fixed parameters method,which will reduce the tracking error.The average reduction of range error between the adaptive joint method and the fixed parameters method is about 0.6 m,while that between the adaptive joint method and the adaptive threshold only method is about 0.3 m.Similar error reduction occurs for the velocity error and acceleration error.展开更多
文摘相比于传统雷达系统,感知雷达能够通过对复杂多变的电磁环境感知自适应调整雷达发射波形,以适应当前的环境,实现推理和判决的优化,从而获得系统性能的大幅提升。从感知雷达的思想出发,研究了杂波环境下的波形自适应选择问题,提出了一种基于修正概率数据关联(modified probabilistic data association,MPDA)的波形自适应选择目标跟踪算法。采用MPDA算法建立和更新杂波下目标的航迹,利用修正的Riccati方程估计下一时刻的滤波协方差矩阵,并推导了相应的波形优化准则函数。仿真表明,该算法降低了密集杂波条件下的滤波误差,相比于未采用波形优化的PDA和MPDA算法,显著提高了跟踪性能。
基金Project(61171133) supported by the National Natural Science Foundation of ChinaProject(11JJ1010) supported by the Natural Science Fund for Distinguished Young Scholars of Hunan Province,China
文摘The joint optimization of detection threshold and waveform parameters for target tracking which comes from the idea of cognitive radar is investigated for the modified probabilistic data association(MPDA)filter.The transmitted waveforms and detection threshold are adaptively selected to enhance the tracking performance.The modified Riccati equation is adopted to predict the error covariance which is used as the criterion function,while the optimization problem is solved through the genetic algorithm(GA).The detection probability,false alarm probability and measurement noise covariance are all considered together,which significantly improves the tracking performance of the joint detection and tracking system.Simulation results show that the proposed adaptive waveform-detection threshold joint optimization method outperforms the adaptive threshold method and the fixed parameters method,which will reduce the tracking error.The average reduction of range error between the adaptive joint method and the fixed parameters method is about 0.6 m,while that between the adaptive joint method and the adaptive threshold only method is about 0.3 m.Similar error reduction occurs for the velocity error and acceleration error.
