改进的自适应扩展卡尔曼滤波雷达目标跟踪算法
An Improved Adaptive Extended Kalman Filter Algorithm for Radar Target Tracking
杨遵立 1张衡 1吕伟 1余娟 1张从胜1
作者信息
摘要
卡尔曼滤波是雷达目标跟踪场景最常用的目标状态跟踪估计算法,但针对非线性运动模型和噪声模型适配失配后,其滤波算法跟踪精度会出现下降.针对这些问题,提出一种机动目标场景下改进自适应扩展卡尔曼滤波的雷达目标跟踪算法,通过目标位置偏差范围来修正预测的位置信息,使用BP神经网络算法来自适应进行扩展卡尔曼滤波(extended kalman filter,EKF)算法预测信息结果的修正;根据噪声影响情况,提出基于实际情况可调的更新因子,用于进行修正后的EKF预测位置信息、测量信息和修正后的BP-EKF预测信息值的权重处理,基于优化模型,自适应选择最优的位置预测信息.仿真分析表明,所提出的算法在目标跟踪的滤波精度和稳定度都得到提升.
Abstract
Kalman filter(KF)algorithm is the most commonly used algorithm in radar target tracking.However the tracking accuracy of KF filtering algorithm decreases when the adaptation of nonlinear motion model and the noise model mismatches.According to these problems,an improved adaptive extended kalman filter(EKF)algorithm for radar target tracking is proposed in the maneuvering target scene,the predicted position information is corrected through the deviation range of the target position.And then the back-propagation(BP)neural network algorithm is used to adapt to the correction of the predicted information results with the EKF algorithm.According to the noise impact of the actual situation,the adjusted update factor is used for the weight processing of the corrected EKF prediction position information,the measured information and the corrected BP-EKF prediction information value.The optimal location prediction information is adaptively selected based on the optimization model.The simulation results show that the filtering accuracy and stability are improved in target tracking.
关键词
机动目标跟踪/扩展卡尔曼滤波/BP神经网络/更新因子/优化模型Key words
maneuvering target tracking/extended kalman filter/BP neural network/update factor/optimization model引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据