A Linear Source Recovery Method for Underdetermined Mixtures of Uncorrelated AR-Model Signals Without Sparseness

Liu, Benxu; Reju, V. G.; Khong, Andy W. H.

doi:10.1109/tsp.2014.2329646

Cited by 23 publications

(18 citation statements)

References 43 publications

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“…Multi-pass SAR images cannot be exactly equidistantly observed over time since the noise across the image stack is not related to the time order. As a consequence, the use of a time series model, commonly employed in statistical signal processing [28][29][30][31], may not be the most suitable approach to obtain a GSP, and, consequently, resulting in lower performance in a CDA. Additionally, the backscattering of the images in the stack is stable in time, i.e., a sequence of pixels for each position follows a similar pattern, and changes in such behavior are understood as outliers.…”

Section: Introductionmentioning

confidence: 99%

Wavelength-Resolution SAR Ground Scene Prediction Based on Image Stack

Palm

Alves²,

Pettersson³

et al. 2020

Sensors

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This paper presents five different statistical methods for ground scene prediction (GSP) in wavelength-resolution synthetic aperture radar (SAR) images. The GSP image can be used as a reference image in a change detection algorithm yielding a high probability of detection and low false alarm rate. The predictions are based on image stacks, which are composed of images from the same scene acquired at different instants with the same flight geometry. The considered methods for obtaining the ground scene prediction include (i) autoregressive models; (ii) trimmed mean; (iii) median; (iv) intensity mean; and (v) mean. It is expected that the predicted image presents the true ground scene without change and preserves the ground backscattering pattern. The study indicates that the the median method provided the most accurate representation of the true ground. To show the applicability of the GSP, a change detection algorithm was considered using the median ground scene as a reference image. As a result, the median method displayed the probability of detection of 97 % and a false alarm rate of 0.11 / km 2 , when considering military vehicles concealed in a forest.

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Section: Introductionmentioning

confidence: 99%

Wavelength-Resolution SAR Ground Scene Prediction Based on Image Stack

Palm

Alves²,

Pettersson³

et al. 2020

Sensors

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show abstract

“…At present, there are two types of solutions to address this issue. The first type is based on the statistical properties of source signals, such as uncorrelated autoregressive (AR) model signals [19], nonnegative tensor factorization [20], and beamforming based on a minimum mean square error [21]. The second type is based on the analytical method of signal sparsity, which mainly includes chaotic matrix estimation and source signal restoration methods.…”

Section: Introductionmentioning

confidence: 99%

“…× 100% (19) where C 1 is the normal hiding image and C 2 is the hiding image when the value of one pixel in the original image is changed, M × N is the size of the hiding image. NPCR and UACI under the experimental data are shown in Table 2, indicating that the hiding scheme proposed in this paper can effectively resist differential attacks.…”

mentioning

confidence: 99%

Low-Element Image Restoration Based on an Out-of-Order Elimination Algorithm

Xie

Chen

et al. 2019

Entropy

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To reduce the consumption of receiving devices, a number of devices at the receiving end undergo low-element treatment (the number of devices at the receiving end is less than that at the transmitting ends). The underdetermined blind-source separation system is a classic low-element model at the receiving end. Blind signal extraction in an underdetermined system remains an ill-posed problem, as it is difficult to extract all the source signals. To realize fewer devices at the receiving end without information loss, this paper proposes an image restoration method for underdetermined blind-source separation based on an out-of-order elimination algorithm. Firstly, a chaotic system is used to perform hidden transmission of source signals, where the source signals can hardly be observed and confidentiality is guaranteed. Secondly, empirical mode decomposition is used to decompose and complement the missing observed signals, and the fast independent component analysis (FastICA) algorithm is used to obtain part of the source signals. Finally, all the source signals are successfully separated using the out-of-order elimination algorithm and the FastICA algorithm. The results show that the performance of the underdetermined blind separation algorithm is related to the configuration of the transceiver antenna. When the signal is 3 × 4 antenna configuration, the algorithm in this paper is superior to the comparison algorithm in signal recovery, and its separation performance is better for a lower degree of missing array elements. The end result is that the algorithms discussed in this paper can effectively and completely extract all the source signals.

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“…Generally, BP methods expolit the 1 -norm minimization algorithm assuming the source signals (i.e. the rows of matrix S) are independent and identically distributed (i.i.d) [34,39] and their source recovery performance decreases with higher k. Actually, the most conventional USR algorithms like BP algorithm achieve a good separation performance for very high dimensional mixing system (m × n) and they fail when the mixing system dimension is low.…”

Section: Introductionmentioning

confidence: 99%

A Novel Underdetermined Source Recovery Algorithm Based on k-Sparse Component Analysis

Eqlimi

Makkiabadi

Samadzadehaghdam

et al. 2018

Circuits Syst Signal Process

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Sparse component analysis (SCA) is a popular method for addressing underdetermined blind source separation (UBSS) in array signal processing applications. We are motivated by problems that arise in the applications where the sources are densely sparse (i.e. only a limited number of sources are inactive at each time instant). The separation performance of current underdetermined source recovery (USR) solutions, including the relaxation and greedy families, reduces with decreasing the mixing system dimension and increasing the sparsity level (k). In this paper, we present a k-SCA based algorithm that is suitable for USR in low dimensional mixing systems. Assuming the sources are at most (m − 1)-sparse where m is the number of mixtures, the proposed method is capable of recovering the sources from the mixtures given the mixing matrix using a subspace detection framework. Simulation results show that the proposed algorithm achieves better separation performance in k-SCA conditions compared with state of the art USR algorithms such as basis pursuit (BP), minimizing L1-norm (ML1), smoothed L0 (SL0), focal underdetermined system solve (FOCUSS) and orthogonal matching pursuit (OMP).

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A Linear Source Recovery Method for Underdetermined Mixtures of Uncorrelated AR-Model Signals Without Sparseness

Cited by 23 publications

References 43 publications

Wavelength-Resolution SAR Ground Scene Prediction Based on Image Stack

Wavelength-Resolution SAR Ground Scene Prediction Based on Image Stack

Low-Element Image Restoration Based on an Out-of-Order Elimination Algorithm

A Novel Underdetermined Source Recovery Algorithm Based on k-Sparse Component Analysis

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