NDLI: Training-free moving target detection with uncertain a priori knowledge for airborne radar

Content Provider	IET Digital Library
Author	Li, Ming Sun, Guohao Tong, Jun He, Zishu
Abstract	This study examines moving target detection for airborne radar in heterogeneous environments. The non-homogeneity of the clutter may lead to a shortage of training samples in practice. In contrast to detectors assuming rich training data, the authors consider a knowledge-aided treatment for training data-limited scenarios by incorporating a priori knowledge of the clutter subspace. The proposed detector is developed within the generalised likelihood ratio test framework, based on the sparsity of the clutter response in the space-time domain. It does not require training samples and allows the usage of uncertain a priori knowledge of the clutter subspace, which may be erroneous. The maximum likelihood estimate of the target amplitude is obtained via oblique projection, which provides more accurate estimates compared with the conventional orthogonal projection. Moreover, they jointly exploit the sparsity of the clutter signal, the block-Toeplitz nature of the clutter covariance matrix, and the a priori knowledge about the clutter subspace, and develop an atomic norm minimisation approach to achieve super-resolution estimation of the clutter response. The simulation results indicate that the proposed detector outperforms several state-of-the-art methods.
Starting Page	372
Ending Page	380
Page Count	9
ISSN	17518784
Volume Number	14
e-ISSN	17518792
Issue Number	Issue 3, Mar (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/14/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2019.0445
Journal	IET Radar, Sonar & Navigation
Publisher Date	2019-12-12
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Airborne Radar Algebra Clutter Covariance Matrix Clutter Response Clutter Signal Clutter Subspace Computer Vision And Image Processing Technique Covariance Matrices Generalised Likelihood Ratio Test Framework Interpolation And Function Approximation Knowledge-aided Treatment Maximum Likelihood Estimate Maximum Likelihood Estimation Numerical Analysis Object Detection Optical, Image And Video Signal Processing Radar Clutter Radar Detection Radar Equipment Rich Training Data Signal Processing And Detection Statistics System And Application Target Amplitude Toeplitz Matrix Training Data-limited Scenario Training Sample Training-free Moving Target Detection
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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