Sparse subband fusion imaging based on parameter estimation of geometrical theory of diffraction model

In real application, most aerial targets are movable. In this paper, an effective multiple subbands coherent processing method is proposed for moving target. Firstly, an echoed signal model of motion target based on geometrical theory of diffraction is established and the influence of velocity on range profile of the target is analyzed. Secondly, a method based on minimum entropy principle is used to compensate velocity. Then, incoherent factors including a quadratic phase term, a linear phase factor, a fixed factor, and an amplitude difference term are analyzed. Subsequently, efficient methods are applied to estimate other incoherent factors, except that the quadratic term is small enough to be ignored. Finally, the feasibility and performance of the proposed method are investigated through numerical simulation.

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“…After velocity compensation, the pole method proposed in [14] and apFFT method [8] are also applied to process two subband data sets.…”

Section: Computer Simulation Resultsmentioning

confidence: 99%

Multiband Radar Signal Coherent Processing Algorithm for Motion Target

Wang

Zhang

Zhao

et al. 2017

International Journal of Antennas and Propagation

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“…where At high frequencies, according to the geometrical diffraction theory (GDT), the radar backscatter from a target can be accurately represented by an all-pole model [15][16][17]. Let X l1 (f ) = Y l1 (f )/|S 1 (f )| 2 (remove the effect of waveform spectral envelop on solving all-pole model).…”

Section: Clean Signal Reconstructionmentioning

confidence: 99%

A clean signal reconstruction approach for coherently combining multiple radars

Liu

et al. 2018

EURASIP J. Adv. Signal Process.

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Distributed radars have the potential to combine coherently for achieving a high signal-to-noise ratio (SNR) while maintaining a moderate antenna size. The key to coherently combining multiple radars is obtaining accurate coherent parameters (CPs), which are used to adjust the transmitting/receiving time and phase of each radar. One approach for CP estimation is to transmit orthogonal waveforms. However, ideally, orthogonal waveforms occupying the same frequency band may not be found in practice. Cross-correlation energy leakage exists between non-orthogonal waveforms, which seriously impairs the accurate acquisition of CPs. To solve this problem, we propose a clean signal reconstruction approach for CP estimation. This approach reconstructs clean echoes by gradually stripping out the cross-correlation energy leakage with a reconstruction-elimination-reconstruction framework. And CPs are obtained from these reconstructed clean echoes. Since the majority of cross-correction energy leakages are eliminated, enhanced CP estimation performance can be achieved. Verified simulations are designed for a dual radar scenario. Results show that the proposed approach significantly improves the performance of CP estimation while reducing the SNR requirement for coherently combining multiple radars.

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“…The multiband data are modeled with autoregressive (AR) models or autoregressive moving average (ARMA) models over a wide bandwidth according to the scattering behaviors of canonical scatterers. Then the signal models can be estimated with root MUltiple SIgnal Classification (MUSIC) algorithm [1], [3], matrix pencil aproach [4] singular-value decomposition (SVD) [2], sparse Bayesian learning algorithm [5] and support vector machine [6]. In addition, a fusion method that combines all-phase fast Fourier transform (apFFT) and iterative adaptive approach [8] was proposed to fuse the dechirped multiband signals, which is more dedicated to the linear frequency modulated (LFM) signals.…”

Section: Introductionmentioning

confidence: 99%

Improved Microwave Imaging by Wavenumber Domain Multiband Data Fusion

Wang

Aubry

Yarovoy

2018

2018 International Conference on Radar (RADAR)

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A matrix-pencil based wavenumber-domain multiband fusion approach is proposed for improved microwave imaging. The suggested fusion approach is based on the fact from the Born approximation that the wavenumber-domain signal in a given scattering direction can be represented as a sum of the same number of contributions over the whole bandwidth. Utilizing this fact, the wavenumber-domain signal in each radial direction can be modeled as a superposition of damped/undamped exponential functions, where measured multiband data are part of the observations of the whole-band signal. Then the multiband signal fusion is addressed by estimating a unified signal model over the while bandwidth with the matrix pencil approach in the wavenumber domain. By using the estimated signal model, the missing data in the frequency gap can be extrapolated, thus synthesizing an equivalent wideband signal spectrum. After an inverse Fourier transform, the synthesize spectrum leads to a focused image with enhanced resolution. Compared to the existing frequency-domain method, the proposed fusion approach can be used for radar imaging with the signals acquired by either collocated or non-collocated arrays in different frequency bands. The effectiveness and accuracy of the proposed fusion approach are demonstrated through some numerical simulations.

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Sparse subband fusion imaging based on parameter estimation of geometrical theory of diffraction model

Cited by 47 publications

References 11 publications

Multiband Radar Signal Coherent Processing Algorithm for Motion Target

Multiband Radar Signal Coherent Processing Algorithm for Motion Target

A clean signal reconstruction approach for coherently combining multiple radars

Improved Microwave Imaging by Wavenumber Domain Multiband Data Fusion

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