Localization of Two Sound Sources Based on Compressed Matched Field Processing with a Short Hydrophone Array in the Deep Ocean

Cao, Ran; Yang, Kunde; Yang, Qi; Chen, Peng; Sun, Quan; Xue, Rui

doi:10.3390/s19173810

Cited by 5 publications

(3 citation statements)

References 30 publications

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“…The fast developed Compressive Sensing (CS) technique has been widely used in the fields of sparse signal reconstruction [1,2], communication channel estimation [3,4], and underwater source localization [5]. Among the well-known CS algorithms, the two representative methods are the convex relaxation-based algorithm and the greedy algorithm.…”

Section: Introductionmentioning

confidence: 99%

Sparse signal recovery from noisy measurements via searching forward OMP

Sun

Yang

et al. 2021

Electronics Letters

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Recovering sparse signals from compressed measurements has received much attention in recent years. Considering that measurement errors always exist, an improved orthogonal matching pursuit (OMP) method which is called Searching Forward OMP (SFOMP), is proposed in this letter. The proposed SFOMP method is designed for compressive sensing and sparse signal recovery in the noisy environment. To improve the recovery performance, the SFOMP method incorporates a searching forward strategy to find the column leading to a minimum norm of residual error among the added candidates in each iteration. Numerical results show that, compared with other commonly used methods, this method provides a higher recovery signal‐to‐noise ratio, more accurate reconstruction of support set, and a competitive computational complexity with noisy measurements.

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Section: Introductionmentioning

confidence: 99%

Sparse signal recovery from noisy measurements via searching forward OMP

Sun

Yang

et al. 2021

Electronics Letters

Self Cite

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“…Sound source localization has high demand and exceptional value in applications such as automobiles [ 1 ], submarines [ 2 ], aircrafts [ 3 , 4 ], etc. Several methods have been proposed, such as beamforming and inverse methods [ 5 , 6 ], in which beamforming has evolved into one of the most important methods of sound source localization.…”

Section: Introductionmentioning

confidence: 99%

Achieving 3D Beamforming by Non-Synchronous Microphone Array Measurements

Guo

Chu

et al. 2020

Sensors

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Beamforming technology is an essential method in acoustic imaging or reconstruction, which has been widely used in sound source localization and noise reduction. The beamforming algorithm can be described as all microphones in a plane simultaneously recording the source signal. The source position is then localized by maximizing the result of the beamformer. Evidence has shown that the accuracy of the sound source localization in a 2D plane can be improved by the non-synchronous measurements of moving the microphone array. In this paper, non-synchronous measurements are applied to 3D beamforming, in which the measurement array envelops the 3D sound source space to improve the resolution of the 3D space. The entire radiated object is covered better by a virtualized large or high-density microphone array, and the range of beamforming frequency is also expanded. The 3D imaging results are achieved in different ways: the conventional beamforming with a planar array, the non-synchronous measurements with orthogonal moving arrays, and the non-synchronous measurements with non-orthogonal moving arrays. The imaging results of the non-synchronous measurements are compared with the synchronous measurements and analyzed in detail. The number of microphones required for measurement is reduced compared with the synchronous measurement. The non-synchronous measurements with non-orthogonal moving arrays also have a good resolution in 3D source localization. The proposed approach is validated with a simulation and experiment.

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“…Compared with scalar hydrophone, vector hydrophone can obtain more sound field information, not only the scalar information of the sound field, but also the vector information of the vibration velocity. The application of vector hydrophone array has been studied in the articles [37][38][39][40][41][42][43][44], but there are little articles focused on the vector hydrophone array design. For the vector hydrophone array, the above-mentioned array design methods still have the following problem which needs to be solved urgently: Previous works involving CS and sparse array design tended to focus on the case of scalar hydrophone arrays; that is, the above optimization method regards each channel of the vector hydrophone array element as one optimized position.…”

Section: Introductionmentioning

confidence: 99%

Vector Hydrophone Array Design Based on Off-Grid Compressed Sensing

Shi

Liang

Qiu

et al. 2020

Sensors

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Array design is the primary consideration for array signal processing, and sparse array design is an important and challenging task. In underwater acoustic environments, the vector hydrophone array contains more information than the scalar hydrophone array, but there are few articles focused on the design of the vector hydrophone array. The difference between the vector hydrophone array and the scalar hydrophone array is that each vector hydrophone has three or four channels. When designing a sparse vector hydrophone array, these channels need to be optimized at the same time to ensure the sparsity of the array elements’ number. To solve this problem, this paper introduced the compressed sensing (CS) theory into the vector hydrophone array design, constructed the vector hydrophone array design problem into a globally solvable optimization problem, proposed a CS-based algorithm with the L1 norm suitable for vector hydrophone array, and realized the simultaneous optimization of multiple channels from the same vector hydrophone. At the same time, the off-grid algorithm was added to obtain higher design accuracy. Two design examples verify the effectiveness of the proposed method. The theoretical analysis and simulation results show that compared with the conventional compressed sensing algorithm with the same aperture, the algorithm proposed in this paper used fewer vector hydrophone elements to obtain better fitting of the desired beam pattern.

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Localization of Two Sound Sources Based on Compressed Matched Field Processing with a Short Hydrophone Array in the Deep Ocean

Cited by 5 publications

References 30 publications

Sparse signal recovery from noisy measurements via searching forward OMP

Sparse signal recovery from noisy measurements via searching forward OMP

Achieving 3D Beamforming by Non-Synchronous Microphone Array Measurements

Vector Hydrophone Array Design Based on Off-Grid Compressed Sensing

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