高斯非平稳机动目标波达角模型及跟踪

doi:10.7527/S1000-6893.2015.0019

电子与控制

本期目录 | 过刊浏览 | 高级检索

前一篇 | 后一篇

高斯非平稳机动目标波达角模型及跟踪

虞翔, 张建秋

复旦大学电子工程系, 上海 200433

收稿日期:2014-11-26 修回日期:2015-01-13 出版日期:2015-10-15 发布日期:2015-01-30
通讯作者: 张建秋, Tel.: 021-55664227 E-mail: jqzhang@fudan.ac.cn E-mail:jqzhang@fudan.ac.cn
作者简介:虞翔男, 博士研究生。主要研究方向: 阵列信号处理及应用。 Tel: 021-55664227 E-mail: xiangyu10@fudan.edu.cn;张建秋男, 博士, 教授, 博士生导师。主要研究方向: 信号处理及其在通信、控制、测量、图像和雷达中的应用。 Tel: 021-55664227 E-mail: jqzhang@fudan.ac.cn
基金资助:
国家自然科学基金 (61171127)

DOA model and tracking for Gaussian non-stationary maneuvering targets

YU Xiang, ZHANG Jianqiu

Department of Electronic Engineering, Fudan University, Shanghai 200433, China

Received:2014-11-26 Revised:2015-01-13 Online:2015-10-15 Published:2015-01-30
Supported by:
National Natural Science Foundation of China (61171127)

摘要/Abstract

摘要：

在实际的跟踪情况中,由于环境条件、目标反射截面等因素的变化,回波信号的功率会随时间变化,即不满足通常阵列信号处理中对高斯信号作平稳性的假设。针对复杂运动条件下高斯非平稳目标的跟踪问题,提出了一种新的机动目标波达角(DOA)模型。该模型全面地刻画了高斯非平稳机动目标的动态,并将目标的DOA和信号功率作为状态变量进行了联合考虑,同时运用虚拟阵列的表示方法构建了相应的观测方程。对于建立的新模型,最后采用无迹卡尔曼滤波(UKF)的框架完成了整个跟踪算法。分析和仿真结果表明,当高斯非平稳机动目标之间存在长时间相互接近的情况时,新方法仍然可以获得较好的跟踪性能。

关键词: 阵列信号处理, 波达角, 多目标跟踪, 高斯非平稳信号, 虚拟阵列, 无迹卡尔曼滤波

Abstract:

In practical applications, due to the variation of environment, radar cross section and other variable factors, the echo power changes over time, which is inconsistent with the stationary assumption of Gaussian signals in the general array signal processing. To address the tracking problem of Gaussian non-stationary maneuvering targets with complex movements, a new direction-of-arrival (DOA) model is proposed. This model captures the dynamic state of Gaussian non-stationary maneuvering targets completely, considering the DOA and signal power as a joint state vector. Meanwhile, this model utilizes virtual array representing method and constructs the observation equation accordingly. Finally, the unscented Kalman filter (UKF) algorithm is used to complete the whole tracking process. Both analyses and simulation results show that the new method is still able to achieve good tracking performance when the Gaussian non-stationary maneuvering targets are close to each other for a long time.

Key words: array signal processing, direction of arrival, multi-target tracking, Gaussian non-stationary signal, virtual array, unscented Kalman filter

中图分类号:

虞翔, 张建秋. 高斯非平稳机动目标波达角模型及跟踪[J]. 航空学报, 2015, 36(10): 3430-3438.

YU Xiang, ZHANG Jianqiu. DOA model and tracking for Gaussian non-stationary maneuvering targets[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2015, 36(10): 3430-3438.

参考文献

[1] Johnson D H, Dudgeon D E. Array signal processing-concepts and techniques[M]. Englewood Cliffs, NJ: Prentice-Hall, 1993: 424-467.
[2] Sword C K, Simaan M, Kamen W W. Multiple target angle tracking using sensor array output[J]. IEEE Transactions on Aerospace and Electronic Systems, 1990, 26(2): 367-372.
[3] Park S B, Ryu C S, Lee K K. Multiple target tracking algorithm using predicted angles[J]. IEEE Transactions on Aerospace and Electronic Systems, 1994, 30(2): 643-648.
[4] Picinbono B. Random signals and systems[M]. Englewood Cliffs, NJ: Prentice-Hall, 1993: 177-180.
[5] Gan H D, Li Z S, Li L, et al. Algorithm for DOA tracking based on adaptive subspace estimation[J]. Technical Acoustics, 2004, 23(4): 214-217 (in Chinese). 淦华东, 李志舜, 李乐, 等. 基于自适应子空间估计的DOA跟踪算法[J]. 声学技术, 2004, 23(4): 214-217.
[6] Ryu C S, Lee S H, Lee K K. Multiple target angle tracking algorithm using innovation extracted from signal subspace[J]. Electronics Letters, 1999, 35(18): 1520-1522.
[7] Javier S A, Sylvie M. An efficient PASTd-algorithm implementation for multiple direction of arrival tracking[J]. IEEE Transactions on Signal Processing, 1999, 47(8): 2321-2324.
[8] Valizadeh A, Karimi M, Oliaei O. An efficient algorithm for multiple direction of arrival tracking based on the constrained projection approximation approach and kalman filter[C]//ICASSP2007. Piscataway, NJ: IEEE Press, 2007: 1089-1092.
[9] Hou S Y, Wu C S, Hung H S, et al. Improved tracking algorithm for multiple targets[C]//OCEANS-MTS/IEEE Kobe Techno-Ocean. Piscataway, NJ: IEEE Press, 2008: 1-4.
[10] Chonavel T, Champagne B, Riou C. Fast adaptive eigenvalue decomposition: A maximum likelihood approach[J]. Signal Processing, 2003, 83: 307-324.
[11] Yang B. Projection approximation subspace tracking[J]. IEEE Transactions on Signal Processing, 1995, 43(1): 95-107.
[12] Badeau R, Abed-Meraim K, Richard G, et al. Sliding window orthonormal PAST algorithm[C]//ICASSP2003. Piscataway, NJ: IEEE Press, 2003: 261-264.
[13] Dogan M C, Mendel J M. Applications of cumulants to array processing—Part I: Aperture extension and array calibration[J]. IEEE Transactions on Signal Processing, 1995, 43(5): 1200-1216.
[14] Chevalier P, Ferreol A. On the virtual array concept for the fourth-order direction finding problem[J]. IEEE Transactions on Signal Processing, 1999, 47(9): 2592-2595.
[15] Hartikainen J, Solin A, Sarkka S. Optimal filtering with kalman filters and smoothers: A manual for the matlab toolbox EKF/UKF[M]. Espoo: Aalto University, 2011: 26-33.
[16] Kong D, Chun J. A fast DOA tracking algorithm based on the extended Kalman filter[C]//NAECON 2000. Piscataway, NJ: IEEE Press, 2000: 235-238.
[17] Moffet A. Minimum-redundancy linear arrays[J]. IEEE Transactions on Antennas and Propagation, 1968, 16(2): 172-175.
[18] Pal P, Vaidyanathan P P. Nested arrays: A novel approach to array processing with enhanced degrees of freedom[J]. IEEE Transactions on Signal Processing, 2010, 58(8): 4167-4181.
[19] Pal P, Vaidyanathan P P. Coprime sampling and the MUSIC algorithm[C]//The 14th IEEE DSP Workshop. Piscataway, NJ: IEEE Press, 2011: 289-294.
[20] Pan J, Zhou J J, Wang F. 2-D DOA estimation for sparse uniform circular array in presence of unknown nonuniform noise[J]. Acta Aeronautica et Astronautica Sinica, 2011, 32(3): 448-456 (in Chinese). 潘捷, 周建江, 汪飞. 非均匀噪声稀疏均匀圆阵的二维DOA估计[J]. 航空学报, 2011, 32(3): 448-456.
[21] Schmidt R O. Multiple emitter location and signal parameter estimation[J]. IEEE Transactions on Antennas and Propagation, 1986, 34(3): 276-280.
[22] Zhang H M, Deng Z L, Lin Y R. UKF method based on model error prediction[J]. Acta Aeronautica et Astronautica Sinica, 2004, 25(6): 598-601 (in Chinese). 张红梅, 邓正隆, 林玉荣. 一种基于模型误差预测的UKF方法[J]. 航空学报, 2004, 25(6): 598-601.

E-mail：hkxb@buaa.edu.cn

关于我们

期刊社服务

专业学科

封面文章

友情链接

主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

高斯非平稳机动目标波达角模型及跟踪

DOA model and tracking for Gaussian non-stationary maneuvering targets

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	王传云, 苏阳, 王琳霖, 王田, 王静静, 高骞. 面向反制无人机集群的多目标连续鲁棒跟踪算法[J]. 航空学报, 2024, 45(7): 329017-329017.
[2]	苑玉彬, 吴一全, 赵朗月, 陈金林, 赵其昌. 基于深度学习的无人机航拍视频多目标检测与跟踪研究进展[J]. 航空学报, 2023, 44(18): 28334-028334.
[3]	靳标, 邝晓飞, 彭宇, 张贞凯. 基于合作博弈的组网雷达分布式功率分配方法[J]. 航空学报, 2022, 43(1): 324776-324776.
[4]	黄景帅, 李永远, 汤国建, 包为民. 高超声速滑翔目标自适应跟踪方法[J]. 航空学报, 2020, 41(9): 323786-323786.
[5]	刘一鸣, 盛文, 胡冰, 张磊. 相控阵雷达长时跟踪波束调度与波形优化策略[J]. 航空学报, 2020, 41(3): 323519-323519.
[6]	田晨, 裴扬, 侯鹏, 赵倩. 基于决策不确定性的多目标跟踪传感器管理[J]. 航空学报, 2020, 41(10): 323781-323781.
[7]	逯志宇, 王建辉, 巴斌, 王大鸣. 修正容积卡尔曼滤波数据域直接定位算法[J]. 航空学报, 2018, 39(9): 322031-322038.
[8]	闫涛, 韩崇昭, 张光华. 空中目标传感器管理方法综述[J]. 航空学报, 2018, 39(10): 22209-022209.
[9]	彭华甫, 黄高明, 田威, 邱昊. 幅度及多普勒信息辅助的多目标跟踪算法[J]. 航空学报, 2018, 39(10): 322247-322247.
[10]	虞翔, 李旦, 张建秋. 鲁棒成形极化敏感阵列波束的方法及极化估计[J]. 航空学报, 2017, 38(6): 320752-320752.
[11]	曾文浩, 朱晓华, 李洪涛, 陈诚, 马义耕. 基于矩阵填充的二维自适应波束形成算法[J]. 航空学报, 2016, 37(5): 1573-1579.
[12]	沈海鸥, 王布宏. 扩展酉矩阵束算法实现稀疏可重构天线阵的优化设计[J]. 航空学报, 2016, 37(12): 3811-3820.
[13]	徐从安, 刘瑜, 熊伟, 宋瑞华, 李天梅. 新生目标强度未知的双门限粒子PHD滤波器[J]. 航空学报, 2015, 36(12): 3957-3969.
[14]	沈志博, 赵国庆, 董春曦, 黄龙. 基于压缩感知的频率和DOA联合估计算法[J]. 航空学报, 2014, 35(5): 1357-1364.
[15]	张宇, 闫云聚, 余龙, 王建强. 阵列宽带Lamb波在结构损伤检测中的应用[J]. 航空学报, 2014, 35(3): 780-787.