Full Text:
<230>
CLC number:
On-line Access: 2023-02-02
Received: 2022-06-17
Revision Accepted: 2023-01-12
Crosschecked: 0000-00-00
Cited: 0
Clicked: 287
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. High-accuracy target tracking for multistatic passive radar based on a deep feedforward neural network[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .
@article{title="High-accuracy target tracking for multistatic passive radar based on a deep feedforward neural network",
author="Baoxiong XU, Jianxin YI, Feng CHENG, Ziping GONG, Xianrong WAN",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200260"
}
%0 Journal Article
%T High-accuracy target tracking for multistatic passive radar based on a deep feedforward neural network
%A Baoxiong XU
%A Jianxin YI
%A Feng CHENG
%A Ziping GONG
%A Xianrong WAN
%J Journal of Zhejiang University SCIENCE C
%V -1
%N -1
%P
%@ 2095-9184
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200260
TY - JOUR
T1 - High-accuracy target tracking for multistatic passive radar based on a deep feedforward neural network
A1 - Baoxiong XU
A1 - Jianxin YI
A1 - Feng CHENG
A1 - Ziping GONG
A1 - Xianrong WAN
J0 - Journal of Zhejiang University Science C
VL - -1
IS - -1
SP -
EP -
%@ 2095-9184
Y1 - 1998
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2200260
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 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 the proposed MDFNN, a filter layer is introduced to describe the temporal sequence relationship of the input measurement sequence, and the optimal measurement sequence size is also analysed. Simulations and field experimental data of the passive radar validate that the accuracy of the proposed algorithm is better than that of the extended Kalman filter (EKF), unscented Kalman filter (UKF), and recurrent neural network (RNN)-based tracking method under the considered scenarios.
Open peer comments: Debate/Discuss/Question/Opinion
<1>