Direct Tracking for a Non-Circular Source Based on CNN+BiLSTM Neural Network in the Presence of Modeling Errors

YIN Jie-xin; WANG Ding; YANG Xin; YANG Bin

doi:10.12263/DZXB.20230525

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Direct Tracking for a Non-Circular Source Based on CNN+BiLSTM Neural Network in the Presence of Modeling Errors

PAPERS | 更新时间：2025-12-08

- Direct Tracking for a Non-Circular Source Based on CNN+BiLSTM Neural Network in the Presence of Modeling Errors
- ACTA ELECTRONICA SINICA Vol. 52, Issue 4, Pages: 1315-1329(2024)
- 作者机构：
  
  1.中国人民解放军战略支援部队信息工程大学信息系统工程学院,河南郑州 450001
  2.国家数字交换系统工程技术研究中心,河南郑州 450002
  3.郑州大学计算机与人工智能学院,河南郑州 450001
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(61901526;62171469;62071029);Youth Talent Recruitment Project in the Military Science and Technology Field(2022-JCJQ-QT-028);Special Technical Project for Pre-research of Military Shared Information System Equipment(315087701)
- DOI：10.12263/DZXB.20230525
  CLC： TN911.7
- Received：08 June 2023，
  
  Revised：2024-01-01，
  
  Published：25 April 2024
- 稿件说明：
移动端阅览
尹洁昕,王鼎,杨欣,等.模型误差影响下基于CNN+BiLSTM神经网络的非圆信号目标直接跟踪算法[J].电子学报,2024,52(04):1315-1329.

YIN Jie-xin, WANG Ding, YANG Xin, et al.Direct Tracking for a Non-Circular Source Based on CNN+BiLSTM Neural Network in the Presence of Modeling Errors[J].Acta Electronica Sinica, 2024, 52(04): 1315-1329.
尹洁昕,王鼎,杨欣,等.模型误差影响下基于CNN+BiLSTM神经网络的非圆信号目标直接跟踪算法[J].电子学报,2024,52(04):1315-1329. DOI：10.12263/DZXB.20230525

YIN Jie-xin, WANG Ding, YANG Xin, et al.Direct Tracking for a Non-Circular Source Based on CNN+BiLSTM Neural Network in the Presence of Modeling Errors[J].Acta Electronica Sinica, 2024, 52(04): 1315-1329. DOI：10.12263/DZXB.20230525

摘要

针对运动观测阵列状态误差与接收频率抖动同时影响下的非圆信号无源跟踪问题，提出了一种基于卷积神经网络（Convolutional Neural Network，CNN）+双向长短时记忆网络（Bi-directional Long Short-Term Memory，BiLSTM）的直接跟踪算法.该算法首先利用多运动观测阵列信号各频带间的相关性与辐射源信号的非圆特性，建立模型误差影响下的扩展多站观测矢量；接着利用多个观测时隙内扩展多站观测矢量的信号子空间构造空时特征输入序列；然后设计基于CNN与BiLSTM混合神经网络的直接跟踪模型，通过训练实现对非圆目标的轨迹矢量直接估计.本文算法是从原始数据信号子空间中估计轨迹矢量的直接跟踪模式，相比传统“观测参数估计+滤波轨迹跟踪”的两步估计模式，具有更高的估计精度.由于本文算法在神经网络训练过程中学习到模型误差的信息，因此能够实现对多种误差的校正.仿真结果表明，本文算法较传统两步跟踪算法与现有直接跟踪算法均具有更高的轨迹估计精度，能够明显提升模型误差影响下多站协同跟踪的鲁棒性.

Abstract

In order to track a non-circular source target under the influence of both the state errors of moving observer arrays and frequency jitter deviations

this paper proposes a direct tracking algorithm based on the CNN (Convolutional Neural Network) + BiLSTM (Bi-directional Long Short-Term Memory) neural network. The proposed algorithm first exploits the correlation between the received array signals of multiple moving observers in various frequency bands and the non-circular property of the radiation source signals. Therefore

an extended multi-station observation vector is established in the presence of modeling errors. Then

the non-circular signal subspaces of the extended multi-station observation vectors within multiple observing timeslots are used to form the spatiotemporal feature input sequence. Subsequently

the direct tracking model for a non-circular source target based on CNN and BiLSTM neural network is designed. After training this neural network

the trajectory vector for a non-circular source target in several timeslots can be directly determined. Because the proposed algorithm estimates the target trajectory vector directly from the subspaces of the original array signals

it has higher estimation accuracy compared to the traditional two-step tracking methods which extract measurement parameters and then estimate the trajectory from them. As the information of modeling errors can be learned from training the neural network

this algorithm can achieve the calibration for modeling errors. The simulation results show that the proposed algorithm has higher trajectory estimation accuracy compared to the traditional two-step tracking algorithm and existing direct tracking algorithm. It significantly improves the robustness of multi-station collaborative tracking under the influence of modeling errors.

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references

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