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

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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
- 网络首发：2023-09，
  
  纸质出版：2023-09-25
- 稿件说明：
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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

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