Page 93 - 《应用声学》2020年第1期

第 39 卷 第 1 期 Vol. 39, No. 1
2020 年 1 月 Journal of Applied Acoustics January, 2020

⋄ 李启虎院士八十华诞学术论文 ⋄

水下目标跟踪的改进非线性滤波快速算法 ∗

石桂欣 1,2 鄢社锋 1,2† 刘 宇 1

(1 中国科学院声学研究所 北京 100190)
(2 中国科学院大学 北京 100049)

摘要：为了提升水下目标的跟踪精度，该文研究了测距误差有偏条件下的水下目标跟踪算法，基于水下目标跟
踪中常用的无迹卡尔曼滤波 (UKF) 和容积卡尔曼滤波 (CKF) 算法，改进提出了将偏差系数作为状态变量之
一进行联合估计的跟踪算法。结合水下目标跟踪场景的实际特点，进一步推导了这两种算法在线性状态方程
条件下的简化形式，分别称为 IS-UKF 和 IS-CKF 算法。仿真实验和湖试实验结果表明，与常规无迹卡尔曼滤
波和容积卡尔曼滤波算法相比，提出的两种改进算法 (IS-UKF 和 IS-CKF 算法) 不仅具有同等运算量，而且提
高了目标轨迹跟踪精度。
关键词：水下目标跟踪；容积卡尔曼滤波；无迹卡尔曼滤波
中图法分类号: O427.9 文献标识码: A 文章编号: 1000-310X(2020)01-0089-08
DOI: 10.11684/j.issn.1000-310X.2020.01.011

Improved simplified Unscented/Cubature Kalman filter algorithm for
underwater target tracking system

SHI Guixin 1,2 YAN Shefeng 1,2 LIU Yu 1

(1 Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China)
(2 University of Chinese Academy of Sciences, Beijing 100049, China)

Abstract: The underwater target tracking algorithm with biased ranging error is studied for better perfor-
mance. In this paper, the bias factor is taken as one of the state variables and, hence, two kinds of improved
tracking algorithms based on Unscented Kalman filter (UKF) and Cubature Kalman filter (CKF) are pro-
posed. Furthermore, based on the characteristics of the underwater scene, the simplified forms of the proposed
algorithms are derived under the condition that the target state model is linear, which are named improved sim-
plified UKF (IS-UKF) and improved simplified CKF (IS-CKF), respectively. In contrast to the standard UKF
and CKF algorithms, the proposed IS-UKF and IS-CKF algorithms can significantly improve the accuracy,
while the computation loads maintain.
Keywords: Underwater target tracking; Cubature Kalman filter; Unscented Kalman filter

2019-04-17 收稿; 2019-11-28 定稿
国家自然科学基金项目 (61725106, 61431020)
∗
作者简介: 石桂欣 (1991– ), 女, 山东临沂人, 博士研究生, 研究方向: 信号与信息处理。
† 通信作者 E-mail: sfyan@ieee.org