相关技术

运动声阵列自适应交互多模型无迹粒子滤波

  • 刘恒 ,
  • 刘亚雷 ,
  • 顾晓辉
展开
  • 1 淮南师范学院数学与计算科学系, 安徽淮南 232038
    2 南京理工大学智能弹药技术国防重点学科实验室, 南京 210094

刘恒(1981-),男,山东临沂人,讲师,硕士,研究方向:目标检测与跟踪、智能化技术、信息融合技术。

收稿日期: 2011-12-29

  网络出版日期: 2025-05-29

基金资助

安徽省高校省级优秀青年人才基金(2012SQRL178);安徽高校省级自然科学研究项目(KJ2011Z357)

Adaptive Interacting Multiple Model Unscented Particle Filter for Dynamic Acoustic Array

  • LIU Heng ,
  • LIU Yalei ,
  • GU Xiaohui
Expand
  • 1 Department of Mathematics and Computing Science, Huainan Normal University, Anhui Huainan 232038, China
    2 Ministerial Key Laboratory of ZNDY, Nanjing University of Science and Technology, Nanjing 210094, China

Received date: 2011-12-29

  Online published: 2025-05-29

摘要

为了提高三维运动声阵列在有色噪声环境中对二维机动目标的跟踪精度,提出了一种基于测量残差的自适应交互多模型无迹粒子滤波算法。该算法建立了三维运动声阵列跟踪系统动态模型,通过无迹变换(unscentedtransformation,UT)构造初始粒子概率分布函数,利用测量残差及自适应因子实时修正测量协方差和状态协方差;通过不同算法仿真对比,验证了文中算法在跟踪精度、稳定性及实时性上的有效性。

本文引用格式

刘恒 , 刘亚雷 , 顾晓辉 . 运动声阵列自适应交互多模型无迹粒子滤波[J]. 弹箭与制导学报, 2012 , 32(5) : 152 -156,160 . DOI: 10.15892/j.cnki.djzdxb.2012.05.009

Abstract

In order to improve the tracking accuracy of 3D dynamic acoustic array for 2D maneuvering target in colored noise environment, the adaptive interacting multiple model unscented particle filter algorithm based on measured residual was proposed. The 3D motion acoustic array tracking system dynamic model was established, and initial probability density function was also defined based on unscented transformation, after that, the measured covariance and state covariance were online adjusted by measured residual and adaptive factor. Finally, the Matlab simulation results between different algorithms show the validity and superiority of the presented algorithm in tracking accuracy, stability and real-time capability.

参考文献

[1]
刘亚雷,顾晓辉. 改进的辅助粒子滤波当前统计模型跟踪算法[J]. 系统工程与电子技术,2010,32(6): 1206-1209.
[2]
M L Moran,R J Greenfield,D K Wilson. Acoustic array tracking performance under moderately complex environmental conditions[J]. Applied Acoustics, 2007,68: 1241-1262.
[3]
Y I Wu,K T Wong,S Lau. The acoustic vector-sensor’s near-field array-manifold[J]. IEEE Trans. on Signal Processing, 2010, 58(7): 121-125.
[4]
Zhang L,Wu X,Pan Q,et al. Multiresolution modeling and estimation of multisensor data[J]. IEEE Trans. on Signal Processing,2004, 52(11): 3170-3182.
[5]
刘亚雷,顾晓辉. 智能子弹对声目标CACEMD-VDAKF 跟踪算法研究[J]. 仪器仪表学报,2011,32(4): 748-755.
[6]
张树春,胡广大. 跟踪机动再入飞行器的交互多模型 Unscented 卡尔曼滤波方法[J]. 自动化学报,2007,33(11): 1220-1226.
[7]
Mazor E,Averbuch A,Bar-Shalom Y,et al. Interacting multiple model methods in target tracking: A survey[J]. IEEE Trans on AES,1998,34(1): 103-123.
[8]
Blom H A,Bar-Shalom Y. The interacting multiple model algorithm for systems with markovian switching coefficient [J]. IEEE Trans on AC,1998,33(8): 780-783.
[9]
宋骊平,姬红. 多站测角的最小二乘交互多模型跟踪算法[J]. 西安电子科技大学学报: 自然科学版,2008,35(2): 242-247.
[10]
Dufour F,Mariton M. Tracking a 3D maneuvering target with passive sensors [J]. IEEE Trans on AES,1991,27(4): 725-739.
[11]
任彪,樊祥,马东辉. 基于多特征融合与粒子滤波的红外弱小目标跟踪方法[J]. 弹箭与制导学报,2009,29(5): 304-307.
[12]
翟永智. 多尺度粒子滤波算法对目标状态估计的研究 [J]. 弹箭与制导学报, 2011, 31(4): 214-217.
[13]
张仲凯,康健,芮国胜. 基于速度约束的粒子滤波算法研究[J]. 弹箭与制导学报, 2010, 30(1): 207-209.
[14]
Doucet A,Godsilli S J,ABDRUEU C. On sequential Monte Carlo sampling methods for Bayesian filtering[J]. Statistics and Computing,2000, 10(3): 197-208.
[15]
YUAN Ze-jian,ZHENG Nan-ning, JIA Xin-chun. The Gauss Hermite particle filter[J]. Acta Electronica Sinica,2003, 31(7): 970-973.
[16]
赵长胜,陶本藻. 有色噪声作用下的卡尔曼滤波[J]. 武汉大学学报: 信息科学版, 2008, 33(2): 180-182.
[17]
王炜,杨露菁. 基于U-D 分解滤波的交互多模型算法 [J]. 情报指挥控制系统与仿真技术,2005,27 (3): 18-22.
文章导航

/