模型参数自适应的低复杂度ATPM-VSIMM算法

曾浩; 母王强; 蒋阳; 杨顺平

doi:10.11959/j.issn.1000-436x.2023186

您当前的位置：

首页 >

文章列表页 >

模型参数自适应的低复杂度ATPM-VSIMM算法

学术论文 | 更新时间：2024-06-06

- 模型参数自适应的低复杂度ATPM-VSIMM算法
- Low-complexity ATPM-VSIMM algorithm with adaptive model parameters
- 通信学报 2023年44卷第9期页码：25-35
- 作者机构：
  
  1. 重庆大学微电子与通信工程学院，重庆 400044
  2. 西南电子技术研究所共性技术部，四川成都 610036
- 作者简介：
  
  [ "曾浩（1977- ），男，四川泸州人，博士，重庆大学教授、博士生导师，主要研究方向为阵列信号处理、无线通信技术" ]
  [ "母王强（1997- ），男，重庆人，重庆大学硕士生，主要研究方向为机动目标跟踪" ]
  [ "蒋阳（1963- ），男，四川岳池人，博士，重庆大学教授、硕士生导师，主要研究方向为现代通信技术与系统" ]
  [ "杨顺平（1976- ），男，四川岳池人，西南电子技术研究所高级工程师，主要研究方向为天线校准和天线测量技术" ]
- 基金信息：
  
  “十四五”国防预研基金资助项目(629010204)
- DOI：10.11959/j.issn.1000-436x.2023186
  中图分类号： TN953
- 网络出版日期：2023-09，
  
  纸质出版日期：2023-09-25
- 稿件说明：
移动端阅览
曾浩, 母王强, 蒋阳, 等. 模型参数自适应的低复杂度ATPM-VSIMM算法[J]. 通信学报, 2023,44(9):25-35.

Hao ZENG, Wangqiang MU, Yang JIANG, et al. Low-complexity ATPM-VSIMM algorithm with adaptive model parameters[J]. Journal on communications, 2023, 44(9): 25-35.
曾浩, 母王强, 蒋阳, 等. 模型参数自适应的低复杂度ATPM-VSIMM算法[J]. 通信学报, 2023,44(9):25-35. DOI： 10.11959/j.issn.1000-436x.2023186.

Hao ZENG, Wangqiang MU, Yang JIANG, et al. Low-complexity ATPM-VSIMM algorithm with adaptive model parameters[J]. Journal on communications, 2023, 44(9): 25-35. DOI： 10.11959/j.issn.1000-436x.2023186.

摘要

在机动目标跟踪中，针对交互式多模型算法使用固定模型集和固定转移概率矩阵导致跟踪精度下降的问题，提出模型参数自适应更新的低复杂度ATPM-VSIMM算法。所提算法根据系统新息变化情况来判断目标是否出现机动，从而调整模型集的状态噪声，实现模型集的自适应更新；然后，根据模型后验概率变化情况和模型间的相互切换关系，准确地计算出转移概率矩阵，从而提高系统运动模型和目标运动轨迹的匹配程度，保证跟踪系统具有滤波精度高和响应速度快的优点。从模型后验概率初值、转移概率矩阵初值和状态噪声三方面验证了所提算法的有效性。仿真结果表明，ATPM-VSIMM算法的空间位置跟踪精度比现有算法提高了8%左右。

Abstract

Aiming at the problem that for maneuvering target tracking

the accuracy of tracking degraded in interacting multiple model algorithms due to the fixed model sets and the fixed transition probability matrix

a low-complexity ATPM-VSIMM algorithm was proposed

which could update the model parameters adaptively.The maneuvering situation of the target was judged according to the innovation changes of the system

and the state noise of the model sets was adjusted to realize the adaptive update of the model sets.Then

the more accurate transition probability matrix was computed through the change of the model posterior probability and the inter-model switching relationship.Therefore

the matching degree between the system motion model and the target motion trajectory was improved.Finally

the high filtering accuracy and the fast response speed of the tracking system were guaranteed.The effectiveness of the proposed algorithm was verified through three aspects that are the initial value of the model posterior probability

the initial value of the transition probability matrix

and the state noise.Simulation results demonstrate that the filtering accuracy of the ATPM-VSIMM algorithm is improved about 8% compared with the existing algorithms.

关键词

Keywords

references

LI X R , JILKOV V P . Survey of maneuvering target tracking.Part V.multiple-model methods [J ] . IEEE Transactions on Aerospace and Electronic Systems , 2005 , 41 ( 4 ): 1255 - 1321 .

BLOM H A P , SHALOM Y B . The interacting multiple model algorithm for systems with Markovian switching coefficients [J ] . IEEE Transactions on Automatic Control , 1988 , 33 ( 8 ): 780 - 783 .

LI X R , SHALOM Y B . Multiple-model estimation with variable structure [J ] . IEEE Transactions on Automatic Control , 1996 , 41 ( 4 ): 478 - 493 .

JIN X B , ROBERT R J , SU T L , et al . The new trend of state estimation:from model-driven to hybrid-driven methods [J ] . Sensors , 2021 , 21 ( 6 ): 2085 - 2106 .

SADEGHZADEH N N , POSHTAN J . Distributed interacting multiple filters for fault diagnosis of navigation sensors in a robotic system [J ] . IEEE Transactions on Systems,Man,and Cybernetics:Systems , 2017 , 47 ( 7 ): 1383 - 1393 .

LI X R , ZHI X R , ZHANG Y M . Multiple-model estimation with variable structure-Part III:model-group switching algorithm [J ] . IEEE Transactions on Aerospace and Electronic Systems , 1999 , 35 ( 1 ): 225 - 241 .

LI X R , ZHANG Y M . Multiple-model estimation with variable structure.V.Likely-model set algorithm [J ] . IEEE Transactions on Aerospace and Electronic Systems , 2000 , 36 ( 2 ): 448 - 466 .

KRISHNAMURTHY V , GAO S J . Syntactic enhancement to VSIMM for roadmap based anomalous trajectory detection:a natural language processing approach [J ] . IEEE Transactions on Signal Processing , 2018 , 66 ( 20 ): 5212 - 5227 .

潘媚媚 , 曹运合 , 王宇 , 等 . 基于机动判别的变结构交互多模型跟踪算法 [J ] . 系统工程与电子技术 , 2019 , 41 ( 4 ): 730 - 736 .

PAN M M , CAO Y H , WANG Y , et al . Variable structure interactive multi-model tracking algorithm based on maneuvering discriminant [J ] . Systems Engineering and Electronics , 2019 , 41 ( 4 ): 730 - 736 .

陈维义 , 何凡 , 刘国强 , 等 . 变结构交互式多模型滤波和平滑算法 [J ] . 系统工程与电子技术 , 2023 , 4 ( 1 ): 1 - 10 .

CHEN W Y , HE F , LIU G Q , et al . A variable structure interactive multiple model filtering and smoothing algorithm [J ] . Systems Engineering and Electronics , 2023 , 4 ( 1 ): 1 - 10 .

ZHANG B L , GAO Y X , DUAN Z S . Variable structure multiple model fixed-interval smoothing [J ] . Chinese Journal of Aeronautics , 2023 , 36 ( 2 ): 139 - 148 .

SUN L F , ZHANG J J , YU H F , et al . Tracking of maneuvering extended target using modified variable structure multiple-model based on adaptive grid best model augmentation [J ] . Remote Sensing , 2022 , 14 ( 7 ): 1613 - 1625 .

ZHU Z L , GUO B , WANG J F . A real-time interactive multi-model target tracking method [C ] // Proceedings of 2021 IEEE International Conference on Artificial Intelligence and Computer Applications . Piscataway:IEEE Press , 2021 : 503 - 507 .

许登荣 , 程水英 , 包守亮 . 自适应转移概率交互式多模型跟踪算法 [J ] . 电子学报 , 2017 , 45 ( 9 ): 2113 - 2120 .

XU D R , CHENG S Y , BAO S L . Interacting multiple model algorithm based on adaptive transition probability [J ] . Acta Electronica Sinica , 2017 , 45 ( 9 ): 2113 - 2120 .

尹聚祺 , 杨震 , 罗亚中 , 等 . 空间机动目标跟踪的改进自适应 IMM算法 [J ] . 系统工程与电子技术 , 2021 , 43 ( 12 ): 3658 - 3666 .

YIN J Q , YANG Z , LUO Y Z , et al . Improved adaptive IMM algorithm for space maneuvering target tracking [J ] . Systems Engineering and Electronics , 2021 , 43 ( 12 ): 3658 - 3666 .

HAN B , HUANG H Q , LEI L , et al . An improved IMM algorithm based on STSRCKF for maneuvering target tracking [J ] . IEEE Access , 2019 , 7 : 57795 - 57804 .

MA J , GUO X T . Combination of IMM algorithm and ASTRWCKF for maneuvering target tracking [J ] . IEEE Access , 2020 , 8 : 143095 - 143103 .

XIE G , SUN L L , WEN T , et al . Adaptive transition probability matrix-based parallel IMM algorithm [J ] . IEEE Transactions on Systems,Man,and Cybernetics:Systems , 2021 , 51 ( 5 ): 2980 - 2989 .

曾浩 , 母王强 , 杨顺平 . 高机动目标跟踪ATPM-IMM算法 [J ] . 通信学报 , 2022 , 43 ( 7 ): 93 - 101 .

ZENG H , MU W Q , YANG S P . High maneuvering target tracking ATPM-IMM algorithm [J ] . Journal on Communications , 2022 , 43 ( 7 ): 93 - 101 .

李昊润 , 卜凡康 , 周剑雄 . 修正的马尔科夫转移矩阵自适应 IMM算法 [J ] . 火力与指挥控制 , 2021 , 46 ( 9 ): 118 - 124 , 132 .

LI H R , BU F K , ZHOU J X . Improved adaptive Markov matrix IMM algorithm [J ] . Fire Control ＆ Command Control , 2021 , 46 ( 9 ): 118 - 124 , 132 .

姜仁华 , 谢洋 , 刘闯 , 等 . 基于改进“当前”统计模型的机动目标IMM跟踪算法 [J ] . 现代雷达 , 2019 , 41 ( 2 ): 39 - 42 .

JIANG R H , XIE Y , LIU C , et al . Maneuvering target IMM algorithm based on improved current statistical model [J ] . Modern Radar , 2019 , 41 ( 2 ): 39 - 42 .

浏览量

272

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

高机动目标跟踪ATPM-IMM算法