基于波形调度的机动目标跟踪算法

针对机动目标的跟踪问题，提出一种结合自适应匀速（Constant Acceleration，CA）模型和波形调度的平方根容积卡尔曼滤波（Square-Root Cubature Kalman Filter，SCKF）算法.在CA模型的基础上，通过构建Jerk分量与速度、加速度的近似关系，使得状态过程噪声与滤波器输出的状态协方差矩阵相联系，以实现模型的自适应调整.另外，利用分数阶傅里叶变换（Fractional Fourier Transform，FrFT）旋转发射波形的模糊函数，使得量测误差椭圆与滤波算法中的状态预测误差椭圆正交，得到最优的发射波形，以从数据处理和信号处理两方面共同提升系统的跟踪性能.仿真结果表明，相比于基于改进当前统计（current statistical，CS）模型的无迹卡尔曼滤波（Unscented Kalman Filter，UKF）算法、基于CS模型的SCKF算法、基于CA模型的SCKF算法和交互式多模型（IMM）SCKF算法，所提算法结构简单且跟踪精度更高.

Maneuvering Target Tracking Based on Waveform Scheduling

Aiming at the maneuvering target tracking problem,a novel square-root cubature Kalman filter (SCKF) is proposed by the integration of the adaptive constant acceleration (CA) model and the waveform scheduling.On the basis of the CA model,the approximation relationship between the Jerk and the velocity as well as the acceleration is established in order to make the connection of the state process noise with the state error covariance matrix.As such,the adaptive adjustment of the proposed model is realized.Additionally,the fractional Fourier transform (FrFT) is utilized to rotate the ambiguity function of the transmitted waveform to maintain the orthogonality between the measurement error ellipse and the state prediction error ellipse.Thereby,the optimal transmitting waveform can be obtained and the tracking performance is systematically improved in both of the data processing and the signal processing.The simulation results show that the proposed algorithm possess a simpler structure and higher accuracy than the unscented Kalman filter based on the modified current statistical (CS) model,the SCKF based on the CS model,the SCKF based on the CA model and the interactive multiple model SCKF (IMM-SCKF).