To improve the tracking accuracy and stability of an optic-electronic target tracking system,the concept of generalized synergic target and an algorithm named error-space estimate method is presented.In this algorithm...To improve the tracking accuracy and stability of an optic-electronic target tracking system,the concept of generalized synergic target and an algorithm named error-space estimate method is presented.In this algorithm,the motion of target is described by guide data and guide errors,and then the maneuver of the target is separated into guide data and guide errors to reduce the maneuver level.Then state estimate is implemented in target state-space and error-space respectively,and the prediction data of target position are acquired by synthesizing the filtering data from target state-space according to kinematic model and the prediction data from errorspace according to guide error model.Differing from typical multi-model method,the kinematic and guide error models work concurrently rather than switch between models.Experiment results show that the performance of the algorithm is better than Kalman filter and strong tracking filter at the same maneuver level.展开更多
针对正交时频空(Orthogonal Time Frequency Space, OTFS)调制系统中均衡器性能不佳及线性滤波器复杂度较高等问题,提出了一种LU(Lower-Upper)分解与迭代最小均方误差(Iterative Minimum Mean Square Error, IMMSE)均衡器结合的OTFS系...针对正交时频空(Orthogonal Time Frequency Space, OTFS)调制系统中均衡器性能不佳及线性滤波器复杂度较高等问题,提出了一种LU(Lower-Upper)分解与迭代最小均方误差(Iterative Minimum Mean Square Error, IMMSE)均衡器结合的OTFS系统信号检测算法(LU-IMMSE)。该算法依据时延多普勒域稀疏信道矩阵的特征,采用一种低复杂度的LU分解方法,以避免MMSE均衡器求解矩阵逆的过程,在保证均衡器性能的前提下降低了均衡器复杂度。在OTFS系统中引入一种IMMSE均衡器,通过不断迭代更新发送符号均值和方差这些先验信息来逼近MMSE均衡器最优估计值。LU-IMMSE算法通过调节迭代次数可以有效降低误比特率。在比特信噪比为8 dB时,5次迭代后的LU-IMMSE均衡器误比特率相比传统的MMSE均衡器降低了约11 dB。随着迭代次数的增大,较传统IMMSE算法降低了计算复杂度。在最大时延系数为4、符号数为16的情况下,与直接求逆相比,所提出的低复杂度LU分解方法降低了约91.72%的矩阵求逆计算复杂度。展开更多
基金supported by the Hi-Tech Research and Development Program of China.
文摘To improve the tracking accuracy and stability of an optic-electronic target tracking system,the concept of generalized synergic target and an algorithm named error-space estimate method is presented.In this algorithm,the motion of target is described by guide data and guide errors,and then the maneuver of the target is separated into guide data and guide errors to reduce the maneuver level.Then state estimate is implemented in target state-space and error-space respectively,and the prediction data of target position are acquired by synthesizing the filtering data from target state-space according to kinematic model and the prediction data from errorspace according to guide error model.Differing from typical multi-model method,the kinematic and guide error models work concurrently rather than switch between models.Experiment results show that the performance of the algorithm is better than Kalman filter and strong tracking filter at the same maneuver level.
文摘针对正交时频空(Orthogonal Time Frequency Space, OTFS)调制系统中均衡器性能不佳及线性滤波器复杂度较高等问题,提出了一种LU(Lower-Upper)分解与迭代最小均方误差(Iterative Minimum Mean Square Error, IMMSE)均衡器结合的OTFS系统信号检测算法(LU-IMMSE)。该算法依据时延多普勒域稀疏信道矩阵的特征,采用一种低复杂度的LU分解方法,以避免MMSE均衡器求解矩阵逆的过程,在保证均衡器性能的前提下降低了均衡器复杂度。在OTFS系统中引入一种IMMSE均衡器,通过不断迭代更新发送符号均值和方差这些先验信息来逼近MMSE均衡器最优估计值。LU-IMMSE算法通过调节迭代次数可以有效降低误比特率。在比特信噪比为8 dB时,5次迭代后的LU-IMMSE均衡器误比特率相比传统的MMSE均衡器降低了约11 dB。随着迭代次数的增大,较传统IMMSE算法降低了计算复杂度。在最大时延系数为4、符号数为16的情况下,与直接求逆相比,所提出的低复杂度LU分解方法降低了约91.72%的矩阵求逆计算复杂度。
