High-accuracy target tracking for multistatic passive radar based on a deep feedforward neural network

Baoxiong XU; Jianxin YI; Feng CHENG; Ziping GONG; Xianrong WAN

doi:10.1631/FITEE.2200260

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High-accuracy target tracking for multistatic passive radar based on a deep feedforward neural network

Regular Papers | Updated：2023-08-29

- High-accuracy target tracking for multistatic passive radar based on a deep feedforward neural network
- 基于深度前馈神经网络的多基地外辐射源雷达高精度目标跟踪
- Frontiers of Information Technology & Electronic Engineering Vol. 24, Issue 8, Pages: 1214-1230(2023)
- Affiliations：
  
  Electronic Information School, Wuhan University, Wuhan 430072, China
- Author bio：
  
  †E-mail: jessiexu@whu.edu.cn
  ‡Corresponding author
  cwing@whu.edu.cn
- Funds：
- DOI：10.1631/FITEE.2200260
  CLC： TN95
- Received：17 June 2022，
  
  Revised：2023-01-12，
  
  Published：0 August 2023
- Accepted：
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Baoxiong XU, Jianxin YI, Feng CHENG, et al. High-accuracy target tracking for multistatic passive radar based on a deep feedforward neural network[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(8): 1214-1230.
DOI：

Baoxiong XU, Jianxin YI, Feng CHENG, et al. High-accuracy target tracking for multistatic passive radar based on a deep feedforward neural network[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(8): 1214-1230. DOI： 10.1631/FITEE.2200260.

摘要

在雷达系统中，目标跟踪误差主要来自运动模型和非线性量测。在评估跟踪算法时，其跟踪精度是主要衡量准则。为提高跟踪精度，本文将跟踪问题表述为从量测到目标状态的回归模型，提出一种基于改进深度前馈神经网络（MDFNN）的跟踪算法。所提MDFNN跟踪算法引入一种滤波层来描述输入量测序列的时序关系，并分析了最优量测序列长度。仿真和实测的外辐射源雷达数据测试表明，在所考虑的场景下，所提算法跟踪精度优于基于扩展卡尔曼滤波器（EKF）、无迹卡尔曼滤波器（UKF）和递归神经网络（RNN）的跟踪方法。

Abstract

In radar systems

target tracking errors are mainly from motion models and nonlinear measurements. When we evaluate a tracking algorithm

its tracking accuracy is the main criterion. To improve the tracking accuracy

in this paper we formulate the tracking problem into a regression model from measurements to target states. A tracking algorithm based on a modified deep feedforward neural network (MDFNN) is then proposed. In MDFNN

a filter layer is introduced to describe the temporal sequence relationship of the input measurement sequence

and the optimal measurement sequence size is analyzed. Simulations and field experimental data of the passive radar show that the accuracy of the proposed algorithm is better than those of extended Kalman filter (EKF)

unscented Kalman filter (UKF)

and recurrent neural network (RNN) based tracking methods under the considered scenarios.

关键词

Keywords

references

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