Improved Extended Kalman Filter Tracking Method Based On Active Waveguide Invariant Distribution
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摘要: 在复杂的海洋环境中，目标的可知信息受环境噪声、混响等的干扰严重，导致目标跟踪效果较差，而从这些干扰中提取目标的可利用特征及其困难。该文将目标与环境的耦合特征融入目标跟踪算法中，提出了一种基于主动波导不变量分布的改进扩展卡尔曼滤波跟踪方法。首先基于浅海波导中目标散射特性基本理论，推导了收发分置条件下的主动波导不变量表征的数学模型，获得了距离、频率以及主动波导不变量分布的约束关系；然后将该约束加入到扩展卡尔曼滤波的状态向量中，通过增加新的约束来提高目标运动模型与真实目标运动轨迹的契合度进而提高目标跟踪的精度；最后通过仿真实验和实测数据验证了该方法的跟踪性能，结果显示：该方法较常规扩展卡尔曼滤波跟踪方法能够更好地提高目标跟踪精度，仿真中结果的优化率约能达到50%，实测数据处理结果的优化率约在60%左右。Abstract: In the complex Marine environment, the known information of the target is seriously disturbed by environmental noise and reverberation, which leads to poor target tracking effect, and it is difficult to extract the utilizable features of the target from these disturbances. This paper proposes an improved extended Kalman filter tracking method based on active waveguide invariant distribution by integrating the coupling characteristics of the target and environment into the target tracking algorithm. Firstly, based on the basic theory of target scattering in shallow sea waveguides, the mathematical model of invariant representation of active waveguide under the condition of receiving and receiving separation is derived, and the constraint relation of distance, frequency, and invariant distribution of active waveguide is obtained. Then this constraint is added to the state vector of the extended Kalman filter, and the fit degree between the model and the real trajectory of the target is improved by adding new constraints to enhance the precision of target tracking. Finally, the tracking performance of the proposed method is verified by simulation experiments and measured data. The results show that: compared with the conventional extended Kalman filter tracking method, the proposed method can improve the tracking accuracy of the target better. The optimization rate of the simulation results can reach about 50%, and the optimization rate of the measured data processing results is about 60%.
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表 1 3种算法仿真的估计位置与真值误差表(m)
算法名称 估计位置和真值偏差-均值 估计位置和真值偏差-峰值 EKF 0.19 0.25 IEKF 0.13 0.19 ID-EKF 0.09 0.13 
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表 2 算法仿真对比优化表(%)
算法对比名称 均值优化率 峰值优化率 IEKF相对EKF 31.58 24.00 ID-EKF相对EKF 52.63 48.00 ID-EKF相对IEKF 30.77 31.58
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表 3 测试参数及目标
信号参数 目标及其运动状态 信号形式 频率(kHz) 脉冲间隔(ms) 脉宽(ms) 采样率(kHz) 球体目标模型(1.2 m直径)，由近及远运动 LFM 40～80 400 5 512
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表 4 3种算法试验的估计位置与真值误差表(m)
算法名称 估计位置和真值偏差-均值 估计位置和真值偏差-峰值 EKF 0.195 0.256 IEKF 0.142 0.187 ID-EKF 0.079 0.095
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表 5 算法试验对比优化表(%)
算法对比名称 均值优化率 峰值优化率 IEKF相对EKF 27.179 26.953 ID-EKF相对EKF 59.487 62.891 ID-EKF相对IEKF 44.366 49.197
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