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Proceedings ArticleDOI

Noise robust classification of moving vehicles via micro-Doppler signatures

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TLDR
Noise reduction and super-resolution are realized simultaneously via a redundant dictionary based l1 -norm optimization method using returned micro-Doppler signals for robust classification of moving wheeled and tracked vehicles.
Abstract
For robust classification of moving wheeled and tracked vehicles using returned micro-Doppler signals within short dwell time, the influence of receiver white noise and low spectrum resolution are encountered. In this paper, noise reduction and super-resolution are realized simultaneously via a redundant dictionary based l1 -norm optimization method. Experiments based on the measured data are presented, including the analysis of noise reduction performance, and the evaluation of classification robustness for different signal-to-noise ratio cases. The experimental results are also compared with related methods.

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Citations
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Journal ArticleDOI

Review of micro-Doppler signatures

TL;DR: In this paper, the authors present a review of micro-Doppler based on subject type, sensor capabilities, as well as environmental effects, and then propose future research areas for micro doppler.
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Comparison of Different Classifiers for Automatic Target Recognition Systems

TL;DR: A monostatic K-band radar system is used to send and receive continuous electromagnetic signals, which are processed with fast Fourier transform for feature extraction to be applied on an artificial neural network and a support vector machine approach.
Proceedings ArticleDOI

Micro-Doppler Deception Jamming for Tracked Vehicles

TL;DR: This paper proposes a new deception jamming method for tracked vehicles against continuous-wave ground surveillance radar that achieves both translational modulation for rigid parts and micro-Doppler modulation for the caterpillars.
Journal ArticleDOI

Human, Robotics Close Motion Classification Based on the Micro Doppler Signature Using HSVD

TL;DR: A practical result which investigates the classification between these two objects based on the micro-Doppler signatures using an S-band 2.4 GHz radar and the improved Stockwell transform to satisfy the classification.
References
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Book

Compressed sensing

TL;DR: It is possible to design n=O(Nlog(m)) nonadaptive measurements allowing reconstruction with accuracy comparable to that attainable with direct knowledge of the N most important coefficients, and a good approximation to those N important coefficients is extracted from the n measurements by solving a linear program-Basis Pursuit in signal processing.
Journal ArticleDOI

A Tutorial on Support Vector Machines for Pattern Recognition

TL;DR: There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.
Journal ArticleDOI

Micro-Doppler effect in radar: phenomenon, model, and simulation study

TL;DR: In this paper, the micro-Doppler effect was introduced in radar data, and a model of Doppler modulations was developed to derive formulas of micro-doppler induced by targets with vibration, rotation, tumbling and coning motions.
Journal ArticleDOI

Reduction of sidelobe and speckle artifacts in microwave imaging: the CLEAN technique

TL;DR: It is shown that targets much weaker than the sidelobe level can be detected and displayed without the hazard of artifacts and the target dynamic range and the image contrast can be increased by up to twice the signal-to-noise ratio per element.
Journal ArticleDOI

Random Sampling of Sparse Trigonometric Polynomials, II. Orthogonal Matching Pursuit versus Basis Pursuit

TL;DR: In this article, the authors investigate the problem of reconstructing sparse multivariate trigonometric polynomials from few randomly taken samples by Basis Pursuit and greedy algorithms such as Orthogonal Matching Pursuit (OMP) and Thresholding.
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