Sparse Recovery on Intrinsic Mode Functions for the Micro-Doppler Parameters Estimation of Small UAVs

Zhao, Yichao; Su, Yi

doi:10.1109/tgrs.2019.2912019

Cited by 12 publications

(7 citation statements)

References 23 publications

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“…In future, further experimental validation, including using dedicated samples can be investigated on not only imaging the static point targets with only frequency domain features, but also more complex and dynamic targets using timefrequency representations and their micro doppler characteristics [16].…”

Section: Discussionmentioning

confidence: 99%

“…Recently, the models combined with parametric dictionary are proposed in [10], [14]- [17]. They have achieved improvements in SAR imaging [10], SAR autofocusing [14], sparse array imaging [15], moving target imaging [16] and time-frequency analysis [17], by optimizing both the off-grid parameters and the sparse solution. Motived by this idea, in this work, we focus on the off-grid problem of MIMO radar imaging during multiple probing pulses, with the reflection coefficients of the targets varying independently from pulse to pulse where Swerling II model is assumed [18].…”

Section: A Review Of Existing Literature and Motivationmentioning

confidence: 99%

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MIMO Radar Imaging With Multiple Probing Pulses for 2D Off-Grid Targets via Variational Sparse Bayesian Learning

Wen

Chen

Duan³

et al. 2020

IEEE Access

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Spatial sparsity of the target space has been successfully exploited to provide accurate rangeangle images by the methods based on sparse signal reconstruction (SSR) in Multiple-Input Multiple-Output (MIMO) radar imaging applications. The SSR based method discretizes the continuous target space into finite grid points and generates an observation model utilized in image reconstruction. However, inaccuracies in the observation model may cause various degradations and spurious peaks in the reconstructed images. In the process of the image formation, the off-grid problem frequently occurs that the true locations of targets that do not coincide with the computation grid. In this paper, we consider the case that the true location of a target has both range and angle-varying two-dimensional (2D) off-grid errors with a noninformative prior. From a variational Bayesian perspective, an iterative algorithm is developed for joint MIMO radar imaging with orthogonal frequency division multiplexing (OFDM) linear frequency modulated (LFM) waveforms and 2D off-grid error estimation of off-grid targets. The targets during multiple probing pulses are modeled as Swerling II case and a unified generalized inverse Gaussian (GIG) prior is adopted for the target reflection coefficient variance at all snapshots. Furthermore, an approach to reducing the computational workload of the signal recovery process is proposed by using singular value decomposition. Experimental results show that the proposed algorithm is insensitive to noise and has improved accuracy in terms of mean squared estimation error under different computation grid interval. INDEX TERMS sparse Bayesian learning, multiple-input multiple-output (MIMO) radar imaging, multiple probing pulses, orthogonal frequency division multiplexing (OFDM), two-dimensional (2D) off-grid error.

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Section: Discussionmentioning

confidence: 99%

Section: A Review Of Existing Literature and Motivationmentioning

confidence: 99%

MIMO Radar Imaging With Multiple Probing Pulses for 2D Off-Grid Targets via Variational Sparse Bayesian Learning

Wen

Chen

Duan³

et al. 2020

IEEE Access

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“…In the case of detecting small UAVs with a single channel radar, because the RCS ratio of rotor blades to the platform is small, the literature indicates that the Doppler signal would be extracted in the first few IMFs by the empirical mode decomposition [19], whereas the m‐D features are exhibited in subsequent IMFs. Heuristically, owing to the quasidyadic filter property, the extra noise channels of NA‐MEMD serve as a reference in the TF plane and enable a more stable estimate of IMFs in the signal‐channel space, giving more accurate TF analysis.…”

Section: Proposed Isa Methodsmentioning

confidence: 99%

Intrinsic synchrosqueezing analysis on micro‐Doppler features for small unmanned aerial vehicles identification with dual‐channel radar

Zhao

2021

IET Microwaves Antenna & Prop

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The micro-Doppler (m-D) effect depends on the rotation of rotor blades in addition to the translation of the platform. Thus it is a characteristic for identifying small unmanned aerial vehicles (UAVs). However, compared with the Doppler signal induced by the translation of the platform, the m-D signal is weak. In this article, a highly localised dataassociation method, intrinsic synchrosqueezing analysis (ISA), is proposed for estimating m-D characteristics from the returned signal of small UAVs with a dual-channel radar. Employing synchrosqueezing transform on intrinsic mode functions derived from noiseassisted multivariate empirical mode decomposition, the proposed ISA method separates the Doppler signal and enables denoising and sharpening time-frequency representation of the m-D signal. Simulation results confirm the theoretical analysis, showing the feasibility of estimating m-D features in a noisy environment. Applications on field data illustrate brighter prospects for identifying small UAVs.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…With the successful application of micro-motion component separation in ISAR highresolution ISAR imaging and the continuous development of m-D signature refinement analysis methods, a large number of studies have emerged on the extraction of m-D signatures and the estimation of micro-motion parameters in rotary targets, such as the S-method-based Viterbi algorithm [12], the Fourier-Bessel transform with time-frequency analysis [13], the intrinsic mode function-based sparse recovery method [14,15], and the synchrosqueezing phase analysis method [16]. A network of passive radar receivers utilizing multistatic geometry of five passive physical channels was established for the analysis of m-D signatures of helicopters, and the main parameters-such as the number of blades, rotating speed, and length of the blades-can be estimated.…”

Section: Introductionmentioning

confidence: 99%

A Micro-Motion Parameters Estimation Method for Multi-Rotor Targets without a Prior

Ren,

Liang,

Wang

et al. 2024

Remote Sensing

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Multi-rotor aircraft have the advantages of a simple structure, low cost, and flexible operation in the unmanned aerial vehicle (UAV) family, and have developed rapidly in recent years. Radar surveillance and classification of the growing number of multi-rotor aircraft has become a challenging problem due to their low-slow-small (LSS) characteristics. Estimation of the blade number is an important step in distinguishing LSS targets. However, most of the current research on micro-motion parameters estimation has focused on the analysis of rotational frequency, length, and the initial phase of blades with a prior of blade number, affecting its ability to identify LSS targets. In this article, a micro-motion parameters estimation method for multi-rotor targets without a prior is proposed. On the basis of estimating the flashing frequency of the blades, a validation function is constructed through spectral analysis to judge the number of blades, and then the rotational frequency is estimated. The blade length is calculated by estimating the maximum Doppler shift. Moreover, the variational mode decomposition (VMD)-based atomic scaling orthogonal matching pursuit (AS-OMP) method is jointly applied to estimate the blade length when suffering from the low PRF and insufficient SNR conditions. Extensive experiments on the simulated and measured data demonstrate that the proposed method outperforms robust micro-motion parameter estimation capability in low PRF and insufficient SNR conditions compared to the traditional time-frequency analysis methods.

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Sparse Recovery on Intrinsic Mode Functions for the Micro-Doppler Parameters Estimation of Small UAVs

Cited by 12 publications

References 23 publications

MIMO Radar Imaging With Multiple Probing Pulses for 2D Off-Grid Targets via Variational Sparse Bayesian Learning

MIMO Radar Imaging With Multiple Probing Pulses for 2D Off-Grid Targets via Variational Sparse Bayesian Learning

Intrinsic synchrosqueezing analysis on micro‐Doppler features for small unmanned aerial vehicles identification with dual‐channel radar

A Micro-Motion Parameters Estimation Method for Multi-Rotor Targets without a Prior

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