Blind Source Separation: A Performance Review Approach

Taha, Luay Yassin; Abdel-Raheem, Esam

doi:10.1109/icspis57063.2022.10002471

Cited by 11 publications

(4 citation statements)

References 35 publications

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“…BSS involves reconstructing unobserved sources, such as time series and images, from a set of observed signals [34,35]. The term "blind" suggests that separation relies solely on the mixed signals, without prior knowledge of the individual sources or the mixing system.…”

Section: Blind Source Separationmentioning

confidence: 99%

An Advanced Scheme for Radar Clutter Suppression Scheme Based on Blind Source Separation

Wang,

Liu,

Wang

2024

Remote Sensing

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In cluttered electromagnetic environments, radar is often disturbed by varied clutter, making target detection challenging. Therefore, achieving effective clutter suppression is crucial for radar target detection. However, traditional clutter suppression methods face three key challenges: (1) significant degradation in target signal detection performance when the clutter’s Doppler spectrum completely masks the target signal; (2) heavy reliance on prior knowledge for optimal performance; and (3) inherent signal energy loss during clutter suppression. To address these challenges, we propose a clutter suppression scheme based on blind source separation (BSS). Initially, the scheme utilizes parallel principal skewness analysis (PPSA) to process the echo signals in the range domain, which helps in identifying the position of moving targets. Subsequently, PPSA is applied once more to process the moving targets in the Doppler domain, allowing for the precise determination of their relative velocities. Subsequently, we evaluate the scheme’s performance with simulated and real data, comparing it with traditional clutter suppression methods and other BSS techniques. The results confirm the effectiveness of the scheme in clutter suppression.

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Section: Blind Source Separationmentioning

confidence: 99%

An Advanced Scheme for Radar Clutter Suppression Scheme Based on Blind Source Separation

Wang,

Liu,

Wang

2024

Remote Sensing

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“…This approach leverages the characteristics of signals in both the time and frequency domains [19]- [21]. TFA methods, decompose signals in both time and frequency, aiding in the separation of different components within mixed signals.…”

Section: ) Time-frequency Analysis-based Bssmentioning

confidence: 99%

Advances in Time-Frequency Analysis for Blind Source Separation: Challenges, Contributions, and Emerging Trends

Li,

Ramli

2023

IEEE Access

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Blind source separation (BSS) is a critical task in untangling non-stationary signals without prior information. This paper extensively explores diverse time-frequency analysis (TFA) methods within BSS systems over the past decade. It underscores the pivotal role of TFA in dealing with non-stationary signals by characterizing their attributes across time and frequency domains. This approach provides a comprehensive understanding of signal dynamics that surpasses conventional techniques focusing solely on temporal or spectral domains. The paper delves into various TFA methods, investigating their influencing factors and aiding researchers in selecting relevant techniques aligned with their objectives. Furthermore, it comprehensively reviews contemporary research, categorizing BSS algorithms into three classes. The role of commonly used TFA methods in each class is systematically evaluated, identifying their strengths and limitations during different separation stages. The paper addresses challenges in implementing BSS algorithms, particularly in under-determined systems with fewer mixing channels than source signals. It highlights the central role of TFA in overcoming these challenges and enhancing separation outcomes.

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“…However, from the micro point of view, the energy intensity at different timefrequency points varies, which leads to a significant difference in the energy of different sources at some time-frequency points when multiple sources are synthesized at the sensor receiver, and a target playing a dominant role. This phenomenon is called the windowdisjoint orthogonality (WDO) of signals and is widely used in blind signal separation for multi-source resolution and separation [21,22]. The higher the WDO characteristic of the signal in a certain time frequency point, the greater the energy of the dominant signal is compared to the sum of other signals, and the direction estimation result of the frequency point will be biased to the target direction of the dominant signal.…”

Section: Introductionmentioning

confidence: 99%

Underwater Source Counting with Local-Confidence-Level-Enhanced Density Clustering

Chen,

Xue,

Wang

et al. 2023

Sensors

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Source counting is the key procedure of autonomous detection for underwater unmanned platforms. A source counting method with local-confidence-level-enhanced density clustering using a single acoustic vector sensor (AVS) is proposed in this paper. The short-time Fourier transforms (STFT) of the sound pressure and vibration velocity measured by the AVS are first calculated, and a data set is established with the direction of arrivals (DOAs) estimated from all of the time–frequency points. Then, the density clustering algorithm is used to classify the DOAs in the data set, with which the number of the clusters and the cluster centers are obtained as the source number and the DOA estimations, respectively. In particular, the local confidence level is adopted to weigh the density of each DOA data point to highlight samples with the dominant sources and downplay those without, so that the differences in densities for the cluster centers and sidelobes are increased. Therefore, the performance of the density clustering algorithm is improved, leading to an improved source counting accuracy. Experimental results reveal that the enhanced source counting method achieves a better source counting performance than that of basic density clustering.

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Blind Source Separation: A Performance Review Approach

Cited by 11 publications

References 35 publications

An Advanced Scheme for Radar Clutter Suppression Scheme Based on Blind Source Separation

An Advanced Scheme for Radar Clutter Suppression Scheme Based on Blind Source Separation

Advances in Time-Frequency Analysis for Blind Source Separation: Challenges, Contributions, and Emerging Trends

Underwater Source Counting with Local-Confidence-Level-Enhanced Density Clustering

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