Higher-Order-Statistics-Based Direction-of-Arrival Estimation of Multiple Wideband Sources With Single Acoustic Vector Sensor

Agarwal, Ashish; Agrawal, Monika; Kumar, Arun

doi:10.1109/joe.2019.2934211

Cited by 18 publications

(9 citation statements)

References 25 publications

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“…The fourth-order cumulant [51] which preserves that symmetric part of the signal while suppressing gaussian noise can be used to realize the target detection [52] in a low SNR environment, and it is also possible to determine whether to use the fourth-order or the third-order cumulant for estimation [53] according to the symmetry of the PDF. Overall performance of the fourth-order detector is superior to that of the second-order (energy) and third-order detectors [51], [54]- [56], and it can effectively detect transient energy with unknown form and duration [57]- [61].…”

Section: ) Fourth-order Cumulantmentioning

confidence: 99%

“…To increase the number of observable sources, it is proved by experiments that the vector hydrophone data with fourthorder statistics can resolve up to eight sources [61]. By further processing HOS on the order of 2q (when q≥2), the number of sources is ' ()…”

Section: Other Related Technologies In the High-order Domainmentioning

confidence: 99%

“…Diagonal slices of fifth-order cumulants Reduce the amount of calculation [85] The three and one half dimension spectrum Extracting high-order frequency coupling features [86], [87] Higher order moment Autocorrelation model of hydrophone source Observing multiple sources [61], [91], [92] Higher-order moment Kurtosis detection Improve the accuracy and reliability of the algorithm. [88] Cyclic accumulation Diagonal slices of Wigner trispectrum Effective extraction of low-frequency line spectrum components [89] Fifth-order cumulant Generalized cyclic cumulant Dealing with α stable distributed noise [90] Fourth…”

Section: Fifth-order Cumulant 2q-order Cumulantmentioning

confidence: 99%

“…In terms of the fourth-order domain feature extraction technology, the signal detection purpose can be achieved [56]- [61] by virtue of the high fourth-order cumulant (kurtosis) value of non-Gaussian coefficient and small kurtosis of the Gaussian noise coefficient [23], [49]. For example, [61], [62] used the fourth-order cumulant kurtosis to achieve the detection of low-frequency underwater acoustic signals and ship-radiated noise, while [88] used high-order moment kurtosis to statistically characterize the ship radiation noise propagated from the source through ocean medium to receiver, and [39] further extended the designed non-Gaussian noise PDF model by inserting the fourth-order kurtosis. In [51], asymmetric generalized Gaussian PDF construction of low-frequency underwater noise was achieved based on the asymmetric Gaussian PDF using parameters of the third and fourth orders to control the sharpness, standard deviation, and symmetry of the model.…”

Section: Other Higher-order Characteristicsmentioning

confidence: 99%

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High-Order Domain Feature Extraction Technology for Ocean Acoustic Observation Signals: A Review

et al. 2023

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For the acquisition of ocean observation information, the underwater acoustic signal is the only known medium that can propagate over long distances in water. However, the underwater acoustic field environment is highly time-varying and spatially variable, and ocean acoustic observation signals are mixed Gaussian, non-cooperative, and nonlinear. Therefore, feature extraction of underwater target signals has been a challenging research direction. The high-order domain feature extraction method of ocean acoustic observation signal is widely used because of its advantages of anti-Gaussian background noise and preservation of signal phase information. Firstly, this paper analyzes three aspects, including the characteristics of ocean acoustic observation signals, the limitations of second-order domain processing methods, and advantages of high-order domain processing methods. Then, this paper focuses on theoretical advantages and application areas of signal high-order domain feature extraction technology. The technical development and application performance of common feature extraction technology in the field of ocean acoustic observation signal processing by higher-order domain methods in recent years are analyzed in the paper, and the challenges and future trends of higher-order domain feature extraction technology in marine acoustic observation signal processing are discussed in the context of recent studies.

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Section: ) Fourth-order Cumulantmentioning

confidence: 99%

Section: Other Related Technologies In the High-order Domainmentioning

confidence: 99%

Section: Fifth-order Cumulant 2q-order Cumulantmentioning

confidence: 99%

Section: Other Higher-order Characteristicsmentioning

confidence: 99%

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High-Order Domain Feature Extraction Technology for Ocean Acoustic Observation Signals: A Review

et al. 2023

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“…This includes computing the deviation (error) from the known DoA; where the root mean square error (RMSE) is the most widely used metric [31-38, 40-58, 60-63]. However, other metrics can be used like the mean square angular error (MSAE) [88], mean square error (MSE) [89], and maximum root mean square error (MRMSE) [90]. In addition, when the 2D-DoA algorithm is expected to work in the underdetermined case, i.e.…”

Section: Performance Metricsmentioning

confidence: 99%

A Review of Sparse Sensor Arrays for Two-Dimensional Direction-of-Arrival Estimation

et al. 2021

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arrays are fundamental to localization applications. Specifically spares arrays can provide superior direction-of-arrival (DoA) estimation performance with limited number of sensors. There has been increased interest in the research community in designing 2D sparse arrays with performance improvement and complexity reduction. The research efforts are uncoordinated resulting in some repetitions and sometimes conflicting claims. After introducing 2D sparse arrays and their importance, this paper establishes the 2D-DoA estimation model and consolidates the performance metrics. An extensive literature overview of sparse arrays for 2D-DoA estimation is presented with an attempt to categorize existing works. The examined arrays include parallel arrays, L-shaped, V-shaped, hourglass, thermos, nested planer, and coprime planner, to name a few. Existing designs are compared in terms of required number of sensors, degrees of freedom (DOF), algorithm used, associated complexity and aperture size. The focus is on describing the sparse arrays, yet some specific details on DoA estimation algorithms are provided for selected array geometries. Fundamental problems of 2D-DoA estimation are outlined and existing solutions to alleviate these problems are discussed. This should be useful in predicting the estimation performance and required complexity; thus, aiding the decision of selecting a sensor geometry for DoA estimation. This review serves as a starting point for researchers interested in exploring or designing new 2D sparse arrays. It also helps to identify the gaps in the field and avoids unnecessary minor design modifications.

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Synthesis Lectures on Engineering, Science, and Technology

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Higher-Order-Statistics-Based Direction-of-Arrival Estimation of Multiple Wideband Sources With Single Acoustic Vector Sensor

Cited by 18 publications

References 25 publications

High-Order Domain Feature Extraction Technology for Ocean Acoustic Observation Signals: A Review

High-Order Domain Feature Extraction Technology for Ocean Acoustic Observation Signals: A Review

A Review of Sparse Sensor Arrays for Two-Dimensional Direction-of-Arrival Estimation

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