针对小型鸟类目标的基于门控循环单元的扩展卡尔曼跟踪方法  

Gated Recurrent Unit-Based Extended Kalman Tracking Method for Small Bird Targets

作　　者：韩冰 王红昌 苏志刚[1] 郝敬堂[1] 赵欣怡 HAN Bing;WANG Hongchang;SU Zhigang;HAO Jingtang;ZHAO Xinyi(Sino-European Institute of Aviation Engineering,Civil Aviation University of China,Tianjin 300300,China)

机构地区：[1]中国民航大学中欧航空工程师学院,天津300300

出　　处：《信号处理》2024年第5期944-956,共13页Journal of Signal Processing

基　　金：国家自然科学基金青年科学基金(62103417);天津市教委科研计划项目(2022KJ059)。

摘　　要：基于激光雷达的小型鸟类的跟踪监视是一种新的实现机场及其周边空域鸟情监测预警的关键技术。针对激光雷达采样频率低引起的目标状态跟踪误差大、模型适应性低的问题,本文提出了一种基于门控循环单元的目标状态估计扩展卡尔曼跟踪方法。该方法通过融合深度学习网络对非线性运动的预测能力和扩展卡尔曼滤波对于随机噪声的抑制能力,实现了对于无法准确建模的非线性运动鸟类目标在低采样率条件下的跟踪。针对深度学习网络为隐性表达模型难以与扩展卡尔曼融合的问题,提出近似一步转移矩阵估计方法,将深度学习网络的预测转化为显性状态转移模型,使得跟踪方法中预测与滤波估计形成循环迭代。本文在公开的鸽子飞行轨迹数据集上进行仿真验证表明,所提方法在不同采样频率条件下的跟踪效果均优于传统跟踪算法,且在低采样频率下相对于已有方法具有超过25.5%跟踪误差性能提升,所提方法能够实现基于激光雷达的鸟类目标跟踪。The lidar-based tracking and surveillance of small birds is a new key technology for bird monitoring and providing early warnings at an airport and its surrounding airspace.To address the problems of large target state tracking errors and low model adaptability caused by the low sampling frequency of lidar,this paper proposes an extended Kalman tracking method for target state estimation based on gated recurrent units.By fusing the predictive capability of deep-learning networks for non-linear motion and the noise reduction capability of extended Kalman filtering,this method can track bird targets with non-linear motion,which cannot be accurately modeled under low sampling rate conditions.To address the problem that the deep learning network is an implicit expression model that is difficult to fuse with extended Kalman filtering,an approximate one-step transfer matrix estimation method is proposed to transform the prediction of the deep-learning network into an explicit state transfer model,which makes the prediction and filter estimation of the tracking method form a circular iteration.Simulations on a publicly available pigeon flight trajectory dataset showed that the proposed method outperformed traditional tracking algorithms at different sampling frequencies and provided a tracking error performance improvement of more than 25.5%at low sampling frequencies compared to existing methods.

关 键 词：鸟类目标跟踪 门控循环单元网络 扩展卡尔曼滤波 激光雷达 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]