Paper Info
Title
Deep Neural Network-Aided Coherent Integration Method For Maneuvering Target Detection
Abstract
Generalized Radon-Fourier transform (GRFT) is a classical long-time coherent integration method for radar maneuvering target detection. GRFT, whose core is to achieve motion parameter estimation via searching, can almost reach the optimal detection performance but heavily suffers from the high computational cost. Motivated by the fact that motion parameter estimation is essentially a non-linear mapping from the radar echo to the target's motion parameters, one can use a deep neural network (DNN), a kind of modeling tool that can learn complex mappings from training data, to directly realize this mapping, thus alleviating the heavy computational burden brought by the searching efforts. Based on this idea, a DNN-aided long-time coherent integration algorithm, which can be viewed as a fast implementation of GRFT, is proposed in this paper. More specifically, we first use a pre-trained DNN to roughly estimate the motion parameters of the target to be detected from the radar echo, and then accomplish the coherent integration of the target for detection via a fine grid search in the neighborhood of the obtained rough estimation results. Finally, simulation results demonstrate that the proposed algorithm can achieve the detection performance close to that of GRFT but with a much lower computational cost. (c) 2021 Elsevier B.V. All rights reserved.
Year
DOI
Venue
2021
10.1016/j.sigpro.2021.107966
SIGNAL PROCESSING
Keywords
DocType
Volume
Long-time coherent integration, Maneuvering target detection, Motion parameter estimation, Deep neural network
Journal
182
ISSN
Citations 
PageRank 
0165-1684
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Chunlei Wang101.35
Jibin Zheng213112.74
Bo Jiu37110.88
Hongwei Liu441666.06
Yuchun Shi511.41