A Time–Frequency Domain Blind Source Separation Method for Underdetermined Instantaneous Mixtures

Tianliang, Peng; Chen, Yang; Liu, Zengli

doi:10.1007/s00034-015-0035-3

Cited by 26 publications

(10 citation statements)

References 41 publications

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“…(31) at 1 p , we substitute 1 λ in Eq. (36) and 1 B in Eq. (34) into the previous equations under the constraint…”

Section: Appendixmentioning

confidence: 99%

RSNT-cFastICA for Complex-Valued Noncircular Signals in Wireless Sensor Networks

Deng¹,

Wei²,

Shen³

et al. 2018

KSII TIIS

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This paper presents an architecture for wireless sensor networks (WSNs) with blind source separation (BSS) applied to retrieve the received mixing signals of the sink nodes first. The little-to-no need of prior knowledge about the source signals of the sink nodes in the BSS method is obviously advantageous for WSNs. The optimization problem of the BSS of multiple independent source signals with complex and noncircular distributions from observed sensor nodes is considered and addressed. This paper applies Castella's reference-based scheme to Novey's negentropy-based algorithms, and then proposes a novel fast fixed-point (FastICA) algorithm, defined as the reference-signal negentropy complex FastICA (RSNT-cFastICA) for complex-valued noncircular-distribution source signals. The proposed method for the sink nodes is substantially more efficient than Novey's quasi-Newton algorithm in terms of computational speed under large numbers of samples, can effectively improve the power consumption effeciency of the sink nodes, and is significantly beneficial for WSNs and wireless communication networks (WCNs). The effectiveness and performance of the proposed method are validated and compared with three related BSS algorithms through theoretical analysis and simulations.

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“…(31) at 1 p , we substitute 1 λ in Eq. (36) and 1 B in Eq. (34) into the previous equations under the constraint…”

Section: Appendixmentioning

confidence: 99%

RSNT-cFastICA for Complex-Valued Noncircular Signals in Wireless Sensor Networks

Deng¹,

Wei²,

Shen³

et al. 2018

KSII TIIS

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show abstract

“…As shown in [25], the core iteration step for nc-FastICA is (28) and the corresponding step for our Rnc-FastICA is…”

Section: Complexity Analysismentioning

confidence: 99%

“…A complex independent component analysis (ICA) is used in a wide range of applications in a variety of fields including audio [21,28,32], biomedical [22,27] and other applications [10,12,17,18,33]. Depending on the application used, the sources may be both sub-Gaussian and super-Gaussian, and particularly in the complex domain, can have circular, i.e., rotation-invariant, and noncircular, i.e., rotation-variant, distributions [30].…”

Section: Introductionmentioning

confidence: 99%

Efficient Optimization of Reference-Based Negentropy for Noncircular Sources in Complex ICA

Zhao

Shen

et al. 2016

Circuits Syst Signal Process

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Bingham proposed a complex fast independent component analysis (cFastICA) algorithm to approximate the nengentropy of circular sources using nonlinear functions. Novey proposed extending the work of Bingham using information from a pseudo-covariance matrix for noncircular sources, particularly for sub-Gaussian noncircular signals such as binary phase-shift keying signals. Based on this work, in the present paper we propose a new reference-based contrast function by introducing reference signals into the negentropy, upon which an efficient optimization FastICA algorithm is derived for noncircular sources. This new approach is similar to Novey's nc-FastICA algorithm, but differs in that it is much more efficient in terms of the computational speed, which is significantly notable with a large number of samples. In this study, the local stability of our reference-based negentropy is analyzed and the derivation of our new algorithm is described in detail. Simulations conducted to demonstrate the performance and effectiveness of our method are also described.

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“…In non-cooperative reception scenarios, the reconnaissance system often receives hybrid time-frequency overlapped signals, so blind source separation (BSS) is applied to separate the sources from the observations without a priori knowledge on hybrid procedure and the sources [3]. In practical applications, the number of source signals usually exceeds the number of sensors, and such a scenario constitutes underdetermined blind source separation (UBSS) [4]. The most difficult case in UBSS is that there is only one sensors, and it is called single-channel blind source separation (SBSS) [5].…”

Section: Introductionmentioning

confidence: 99%

Effective Separation Method for Single-Channel Time-Frequency Overlapped Signals Based on Improved Empirical Wavelet Transform

2019

KSII TIIS

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To improve the separation performance of time-frequency overlapped radar and communication signals from a single channel, this paper proposes an effective separation method based on an improved empirical wavelet transform (EWT) that introduces a fast boundary detection mechanism. The fast boundary detection mechanism can be regarded as a process of searching, difference optimization, and continuity detection of the important local minima in the Fourier spectrum that enables determination of the sub-band boundary and thus allows multiple signal components to be distinguished. An orthogonal empirical wavelet filter bank that was designed for signal adaptive reconstruction is then used to separate the input time-frequency overlapped signals. The experimental results show that if two source components are completely overlapped within the time domain and the spectrum overlap ratio is less than 60%, the average separation performance is improved by approximately 32.3% when compared with the classic EWT; the proposed method also improves the suitability for multiple frequency shift keying (MFSK) and reduces the algorithm complexity.

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A Time–Frequency Domain Blind Source Separation Method for Underdetermined Instantaneous Mixtures

Cited by 26 publications

References 41 publications

RSNT-cFastICA for Complex-Valued Noncircular Signals in Wireless Sensor Networks

RSNT-cFastICA for Complex-Valued Noncircular Signals in Wireless Sensor Networks

Efficient Optimization of Reference-Based Negentropy for Noncircular Sources in Complex ICA

Effective Separation Method for Single-Channel Time-Frequency Overlapped Signals Based on Improved Empirical Wavelet Transform

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